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SAGE: Sequential Attribute Generator for Analyzing Glioblastomas Using Limited Dataset

Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Taejin Min, Shiv Bhanu, Bir Bhanu

Auto-TLDR; SAGE: Generative Adversarial Networks for Imaging Biomarker Detection and Prediction

Abstract Slides Poster

While deep learning approaches have shown remarkable performance in many imaging tasks, most of these methods rely on availability of large quantities of data. Medical image data, however, is scarce and fragmented. Generative Adversarial Networks (GANs) have recently been very effective in handling such datasets by generating more data. If the datasets are very small, however, GANs cannot learn the data distribution properly, resulting in less diverse or low-quality results. One such limited dataset is that for the concurrent gain of 19/20 chromosomes (19/20 co-gain), a mutation with positive prognostic value in Glioblastomas (GBM). In this paper, we detect imaging biomarkers for the mutation to streamline the extensive and invasive prognosis pipeline. Since this mutation is relatively rare, i.e. small dataset, we propose a novel generative framework – the Sequential Attribute GEnerator (SAGE), that generates detailed tumor imaging features while learning from a limited dataset. Experiments show that not only does SAGE generate high quality tumors when compared to standard Deep Convolutional GAN (DC-GAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), it also captures the imaging biomarkers accurately.

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Signal Generation Using 1d Deep Convolutional Generative Adversarial Networks for Fault Diagnosis of Electrical Machines

Russell Sabir, Daniele Rosato, Sven Hartmann, Clemens Gühmann

Auto-TLDR; Large Dataset Generation from Faulty AC Machines using Deep Convolutional GAN

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AC machines may be subjected to different electrical or mechanical faults during their operation. Fault patterns can be detected in the DC current from the machine’s E-Drive system with the help of Deep or Machine Learning algorithms. However, Deep or Machine Learning algorithms require large amounts of dataset for training and without the availability of a large dataset the algorithms fail to generalize or give their optimal performance. Collecting large amounts of data from faulty machine can be a tedious task. It is expensive and not always possible. In some cases, the machine is completely damaged even before sufficient amount of data can be collected. Also, data collection from defected machine may cause permanent damage to the connected system. Therefore, in this paper the problem of small dataset is tackled by presenting a methodology for large dataset generation by using the well-known generative model, Generative Adversarial Networks (GAN). As an example, the stator open circuit fault in a synchronous machine is considered. DC currents from the machine’s E-Drive system are measured from different healthy and faulty machines and are used for training of two 1d DCGANs (Deep Convolutional GANs), one for the healthy and the other for the current signal from the faulty machine. Conventional GANs are difficult to train, however in this paper, training parameters of 1d DCGAN are tuned which results an improved training process. The performance of generator during the training of 1d DCGAN is evaluated by using the Fréchet Inception Distance (FID) metric. The proposed 1d DCGAN model is said to converge when FID score between the real and generated signal reaches below a certain threshold. The generated signals from the trained 1d DCGAN are further evaluated using the PDF (Probability Density Function), frequency domain analysis and other measures which check for duplication of the real data and their statistical diversity. The trained 1d DCGAN is able to generate DC current signals for building large datasets for the training of Deep or Machine learning models.

Leveraging Unlabeled Data for Glioma Molecular Subtype and Survival Prediction

Nicholas Nuechterlein, Beibin Li, Mehmet Saygin Seyfioglu, Sachin Mehta, Patrick Cimino, Linda Shapiro

Auto-TLDR; Multimodal Brain Tumor Segmentation Using Unlabeled MR Data and Genomic Data for Cancer Prediction

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In this paper, we address two long-standing challenges in neuro-oncology: (1) how to leverage large amounts of unlabeled magnetic resonance (MR) imaging data for radiogenomic tasks and (2) how to unite glioma MR imaging with genomic data. We examine multi-parametric MR data from 542 patients in the combined training, validation, and testing sets of the 2018 Multimodal Brain Tumor Segmentation Challenge and somatic copy number alteration (SCNA) data from 1090 patients in The Cancer Genome Archive's (TCGA) lower-grade glioma and glioblastoma projects. We propose a novel application of multi-task learning (MTL) that leverages unlabeled MR data by jointly learning tumor segmentation masks with glioma molecular subtype markers and allows for SCNA input when available. There are 235 patients in the intersection of these MR and SCNA datasets, which we divide into an unlabeled training set, a labeled training set, and a validation set. Our MTL model significantly outperforms comparable classification models trained only on labeled MR data for both IDH1/2 mutation and 1p/19q co-deletion glioma subtype marker prediction tasks. We also observe that models trained on genomic and imaging data improve survival prediction results achieved by models trained on either alone. We will release our source code for future research.

3D Medical Multi-Modal Segmentation Network Guided by Multi-Source Correlation Constraint

Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan

Auto-TLDR; Multi-modality Segmentation with Correlation Constrained Network

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In the field of multimodal segmentation, the correlation between different modalities can be considered for improving the segmentation results. In this paper, we propose a multi-modality segmentation network with a correlation constraint. Our network includes N model-independent encoding paths with N image sources, a correlation constrain block, a feature fusion block, and a decoding path. The model-independent encoding path can capture modality-specific features from the N modalities. Since there exists a strong correlation between different modalities, we first propose a linear correlation block to learn the correlation between modalities, then a loss function is used to guide the network to learn the correlated features based on the correlation representation block. This block forces the network to learn the latent correlated features which are more relevant for segmentation. Considering that not all the features extracted from the encoders are useful for segmentation, we propose to use dual attention based fusion block to recalibrate the features along the modality and spatial paths, which can suppress less informative features and emphasize the useful ones. The fused feature representation is finally projected by the decoder to obtain the segmentation result. Our experiment results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate the effectiveness of our proposed method.

Local Facial Attribute Transfer through Inpainting

Ricard Durall, Franz-Josef Pfreundt, Janis Keuper

Auto-TLDR; Attribute Transfer Inpainting Generative Adversarial Network

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The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our Attribute Transfer Inpainting Generative Adversarial Network (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.

A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-Scale Computed Tomography Data

Bo Wang, Zhengqing Xu, Wei Xu, Qingsen Yan, Liang Zhang, Zheng You

Auto-TLDR; The Biggest Treatment-Oriented Liver Cancer Dataset for Segmentation

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How to build a high-performance liver-related computer assisted diagnosis system is an open question of great interest. However, the performance of the state-of-art algorithm is always limited by the amount of data and quality of the label. To address this problem, we propose the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning. This dataset provides 216 cases (totally about 268K frames) scanned images in contrast-enhanced computed tomography (CT). We labeled all the CT images with the liver, liver vasculature and liver tumor segmentation ground truth for train and tune segmentation algorithms in advance. Based on that, we evaluate several recent and state-of-the-art segmentation algorithms, including 7 deep learning methods, on CT sequences. All results are compared to reference segmentations five error metrics that highlight different aspects of segmentation accuracy. In general, compared with previous datasets, our dataset is really a challenging dataset. To our knowledge, the proposed dataset and benchmark allow for the first time systematic exploration of such issues, and will be made available to allow for further research in this field.

On the Evaluation of Generative Adversarial Networks by Discriminative Models

Amirsina Torfi, Mohammadreza Beyki, Edward Alan Fox

Auto-TLDR; Domain-agnostic GAN Evaluation with Siamese Neural Networks

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Generative Adversarial Networks (GANs) can accurately model complex multi-dimensional data and generate realistic samples. However, due to their implicit estimation of data distributions, their evaluation is a challenging task. The majority of research efforts associated with tackling this issue were validated by qualitative visual evaluation. Such approaches do not generalize well beyond the image domain. Since many of those evaluation metrics are proposed and bound to the vision domain, they are difficult to apply to other domains. Quantitative measures are necessary to better guide the training and comparison of different GANs models. In this work, we leverage Siamese neural networks to propose a domain-agnostic evaluation metric: (1) with a qualitative evaluation that is consistent with human evaluation, (2) that is robust relative to common GAN issues such as mode dropping and invention, and (3) does not require any pretrained classifier. The empirical results in this paper demonstrate the superiority of this method compared to the popular Inception Score and are competitive with the FID score.

IDA-GAN: A Novel Imbalanced Data Augmentation GAN

Hao Yang, Yun Zhou

Auto-TLDR; IDA-GAN: Generative Adversarial Networks for Imbalanced Data Augmentation

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Class imbalance is a widely existed and challenging problem in real-world applications such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to the scarce of data, it could significantly deteriorate the accuracy of classification. To address this challenge, we propose a novel Imbalanced Data Augmentation Generative Adversarial Networks (GAN) named IDA-GAN as an augmentation tool to deal with the imbalanced dataset. This is a great challenge because it is hard to train a GAN model under this situation. We overcome this issue by coupling Variational autoencoder along with GAN training. Specifically, we introduce the Variational autoencoder to learn the majority and minority class distributions in the latent space, and use the generative model to utilize each class distribution for the subsequent GAN training. The generative model learns useful features to generate target minority-class samples. By comparing with the state-of-the-art GAN models, the experimental results demonstrate that our proposed IDA-GAN could generate more diverse minority samples with better qualities, and it consistently benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets: MNIST, Fashion-MNIST, SVHN, CIFAR-10 and GTRSB.

Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI

Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason Connolly, Toby Breckon

Auto-TLDR; SIS-GAN: Subject Invariant SSVEP Generative Adversarial Network for Brain-Computer Interface

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Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Electroencephalography (EEG) has provided a non-invasive method of interfacing with a human brain, the acquired data is often heavily subject and session dependent. This makes seamless incorporation of such data into real-world applications intractable as the subject and session data variance can lead to long and tedious calibration requirements and cross-subject generalisation issues. Focusing on a Steady State Visual Evoked Potential (SSVEP) classification systems, we propose a novel means of generating highly-realistic synthetic EEG data invariant to any subject, session or other environmental conditions. Our approach, entitled the Subject Invariant SSVEP Generative Adversarial Network (SIS-GAN), produces synthetic EEG data from multiple SSVEP classes using a single network. Additionally, by taking advantage of a fixed-weight pre-trained subject classification network, we ensure that our generative model remains agnostic to subject-specific features and thus produces subject-invariant data that can be applied to new previously unseen subjects. Our extensive experimental evaluation demonstrates the efficacy of our synthetic data, leading to superior performance, with improvements of up to 16% in zero-calibration classification tasks when trained using our subject-invariant synthetic EEG signals.

Augmentation of Small Training Data Using GANs for Enhancing the Performance of Image Classification

Shih-Kai Hung, John Q. Gan

Auto-TLDR; Generative Adversarial Network for Image Training Data Augmentation

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It is difficult to achieve high performance without sufficient training data for deep convolutional neural networks (DCNNs) to learn. Data augmentation plays an important role in improving robustness and preventing overfitting in machine learning for many applications such as image classification. In this paper, a novel method for data augmentation is proposed to solve the problem of machine learning with small training datasets. The proposed method can synthesise similar images with rich diversity from only a single original training sample to increase the number of training data by using generative adversarial networks (GANs). It is expected that the synthesised images possess class-informative features, which may be in the validation or testing data but not in the training data due to that the training dataset is small, and thus they can be effective as augmented training data to improve classification accuracy of DCNNs. The experimental results have demonstrated that the proposed method with a novel GAN framework for image training data augmentation can significantly enhance the classification performance of DCNNs for applications where original training data is limited.

Combining GANs and AutoEncoders for Efficient Anomaly Detection

Fabio Carrara, Giuseppe Amato, Luca Brombin, Fabrizio Falchi, Claudio Gennaro

Auto-TLDR; CBIGAN: Anomaly Detection in Images with Consistency Constrained BiGAN

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In this work, we propose CBiGAN --- a novel method for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN. Our model exhibits fairly good modeling power and reconstruction consistency capability. We evaluate the proposed method on MVTec AD --- a real-world benchmark for unsupervised anomaly detection on high-resolution images --- and compare against standard baselines and state-of-the-art approaches. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin and performs comparably to expensive state-of-the-art iterative methods while reducing the computational cost. We also observe that our model is particularly effective in texture-type anomaly detection, as it sets a new state of the art in this category. The code will be publicly released.

Semi-Supervised Generative Adversarial Networks with a Pair of Complementary Generators for Retinopathy Screening

Yingpeng Xie, Qiwei Wan, Hai Xie, En-Leng Tan, Yanwu Xu, Baiying Lei

Auto-TLDR; Generative Adversarial Networks for Retinopathy Diagnosis via Fundus Images

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Several typical types of retinopathy are major causes of blindness. However, early detection of retinopathy is quite not easy since few symptoms are observable in the early stage, attributing to the development of non-mydriatic retinal camera. These camera produces high-resolution retinal fundus images provide the possibility of Computer-Aided-Diagnosis (CAD) via deep learning to assist diagnosing retinopathy. Deep learning algorithms usually rely on a great number of labelled images which are expensive and time-consuming to obtain in the medical imaging area. Moreover, the random distribution of various lesions which often vary greatly in size also brings significant challenges to learn discriminative information from high-resolution fundus image. In this paper, we present generative adversarial networks simultaneously equipped with "good" generator and "bad" generator (GBGANs) to make up for the incomplete data distribution provided by limited fundus images. To improve the generative feasibility of generator, we introduce into pre-trained feature extractor to acquire condensed feature for each fundus image in advance. Experimental results on integrated three public iChallenge datasets show that the proposed GBGANs could fully utilize the available fundus images to identify retinopathy with little label cost.

Deep Learning-Based Type Identification of Volumetric MRI Sequences

Jean Pablo De Mello, Thiago Paixão, Rodrigo Berriel, Mauricio Reyes, Alberto F. De Souza, Claudine Badue, Thiago Oliveira-Santos

Auto-TLDR; Deep Learning for Brain MRI Sequences Identification Using Convolutional Neural Network

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The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor. As the interest for automatizing brain volume MRI analysis increases, it becomes convenient to have each sequence well identified. However, the unstandardized naming of MRI sequences make their identification difficult for automated systems, as well as make it difficult for researches to generate or use datasets for machine learning research. In face of that, we propose a system for identifying types of brain MRI sequences based on deep learning. By training a Convolutional Neural Network (CNN) based on 18-layer ResNet architecture, our system is able to classify a volumetric brain MRI as a T1, T1c, T2 or FLAIR sequence, or whether it does not belong to any of these classes. The network was trained with both pre-processed (BraTS dataset) and non-pre-processed (TCGA-GBM dataset) images with diverse acquisition protocols, requiring only a few layers of the volume for training. Our system is able to classify among sequence types with an accuracy of 96.27%.

Automatic Semantic Segmentation of Structural Elements related to the Spinal Cord in the Lumbar Region by Using Convolutional Neural Networks

Jhon Jairo Sáenz Gamboa, Maria De La Iglesia-Vaya, Jon Ander Gómez

Auto-TLDR; Semantic Segmentation of Lumbar Spine Using Convolutional Neural Networks

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This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline.

Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

Martin Kolarik, Radim Burget, Carlos M. Travieso-Gonzalez, Jan Kocica

Auto-TLDR; Planar 3D Res-U-Net Network for Unbalanced 3D Image Segmentation using Fluid Attenuation Inversion Recover

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We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16 which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we evaluate the method on the MICCAI 2016 MS lesion segmentation challenge dataset utilizing solely Fluid Attenuation Inversion Recover (FLAIR) sequence without brain extraction for training and inference to simulate real medical praxis. The planar 3D res-u-net network performed the best both in sensitivity and Dice score amongst end to end methods processing raw MRI scans and achieved comparable Dice score to a state-of-the-art unimodal not end to end approach. Complete source code was released under the open-source license and this paper is in compliance with the Machine learning Reproducibility Checklist. By implementing practical transfer learning for 3D data representation we were able to successfully segment heavily unbalanced data without selective sampling and achieved more reliable results using less training data in single modality. From medical perspective, the unimodal approach gives an advantage in real praxis as it does not require co-registration nor additional scanning time during examination. Although modern medical imaging methods capture high resolution 3D anatomy scans suitable for computer aided detection system processing, deployment of automatic systems for interpretation of radiology imaging is still rather theoretical in many medical areas. Our work aims to bridge the gap offering solution for partial research questions.

Unsupervised Detection of Pulmonary Opacities for Computer-Aided Diagnosis of COVID-19 on CT Images

Rui Xu, Xiao Cao, Yufeng Wang, Yen-Wei Chen, Xinchen Ye, Lin Lin, Wenchao Zhu, Chao Chen, Fangyi Xu, Yong Zhou, Hongjie Hu, Shoji Kido, Noriyuki Tomiyama

Auto-TLDR; A computer-aided diagnosis of COVID-19 from CT images using unsupervised pulmonary opacity detection

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COVID-19 emerged towards the end of 2019 which was identified as a global pandemic by the world heath organization (WHO). With the rapid spread of COVID-19, the number of infected and suspected patients has increased dramatically. Chest computed tomography (CT) has been recognized as an efficient tool for the diagnosis of COVID-19. However, the huge CT data make it difficult for radiologist to fully exploit them on the diagnosis. In this paper, we propose a computer-aided diagnosis system that can automatically analyze CT images to distinguish the COVID-19 against to community-acquired pneumonia (CAP). The proposed system is based on an unsupervised pulmonary opacity detection method that locates opacity regions by a detector unsupervisedly trained from CT images with normal lung tissues. Radiomics based features are extracted insides the opacity regions, and fed into classifiers for classification. We evaluate the proposed CAD system by using 200 CT images collected from different patients in several hospitals. The accuracy, precision, recall, f1-score and AUC achieved are 95.5%, 100%, 91%, 95.1% and 95.9% respectively, exhibiting the promising capacity on the differential diagnosis of COVID-19 from CT images.

FOANet: A Focus of Attention Network with Application to Myocardium Segmentation

Zhou Zhao, Elodie Puybareau, Nicolas Boutry, Thierry Geraud

Auto-TLDR; FOANet: A Hybrid Loss Function for Myocardium Segmentation of Cardiac Magnetic Resonance Images

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In myocardium segmentation of cardiac magnetic resonance images, ambiguities often appear near the boundaries of the target domains due to tissue similarities. To address this issue, we propose a new architecture, called FOANet, which can be decomposed in three main steps: a localization step, a Gaussian-based contrast enhancement step, and a segmentation step. This architecture is supplied with a hybrid loss function that guides the FOANet to study the transformation relationship between the input image and the corresponding label in a threelevel hierarchy (pixel-, patch- and map-level), which is helpful to improve segmentation and recovery of the boundaries. We demonstrate the efﬁciency of our approach on two public datasets in terms of regional and boundary segmentations.

Generative Latent Implicit Conditional Optimization When Learning from Small Sample

Idan Azuri, Daphna Weinshall

Auto-TLDR; GLICO: Generative Latent Implicit Conditional Optimization for Small Sample Learning

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We revisit the long-standing problem of learning from small sample. The generation of new samples from a small training set of labeled points has attracted increased attention in recent years. In this paper, we propose a novel such method called GLICO (Generative Latent Implicit Conditional Optimization). GLICO learns a mapping from the training examples to a latent space and a generator that generates images from vectors in the latent space. Unlike most recent work, which rely on access to large amounts of unlabeled data, GLICO does not require access to any additional data other than the small set of labeled points. In fact, GLICO learns to synthesize completely new samples for every class using as little as 5 or 10 examples per class, with as few as 10 such classes and no data from unknown classes. GLICO is then used to augment the small training set while training a classifier on the small sample. To this end, our proposed method samples the learned latent space using spherical interpolation (slerp) and generates new examples using the trained generator. Empirical results show that the new sampled set is diverse enough, leading to improvement in image classification in comparison with the state of the art when trained on small samples obtained from CIFAR-10, CIFAR-100, and CUB-200.

CardioGAN: An Attention-Based Generative Adversarial Network for Generation of Electrocardiograms

Subhrajyoti Dasgupta, Sudip Das, Ujjwal Bhattacharya

Auto-TLDR; CardioGAN: Generative Adversarial Network for Synthetic Electrocardiogram Signals

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Electrocardiogram (ECG) signal is studied to obtain crucial information about the condition of a patient's heart. Machine learning based automated medical diagnostic systems that may help to evaluate the condition of the heart from this signal are required to be trained using large volumes of labelled training samples and the same may increase the chance of compromising with the patients' privacy. To solve this issue, generation of synthetic electrocardiogram signals by learning only from the general distributions of the available real training samples have been attempted in the literature. However, these studies did not pay necessary attention to the specific vital details of these signals, such as the P wave, the QRS complex, and the T wave. This shortcoming often results in the generation of unrealistic synthetic signals, such as a signal which does not contain one or more of the above components. In the present study, a novel deep generative architecture, termed as CardioGAN, based on generative adversarial network and powered by the effective attention mechanism has been designed which is capable of learning the intricate inter-dependencies among the various parts of real samples leading to the generation of more realistic electrocardiogram signals. Also, it helps in reducing the risk of breaching the privacy of patients. Extensive experimentation performed by us establishes that the proposed method achieves a better performance in generating synthetic electrocardiogram signals in comparison to the existing methods. The source code will be made available on github.

Attributes Aware Face Generation with Generative Adversarial Networks

Zheng Yuan, Jie Zhang, Shiguang Shan, Xilin Chen

Auto-TLDR; AFGAN: A Generative Adversarial Network for Attributes Aware Face Image Generation

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Recent studies have shown remarkable success in face image generations. However, most of the existing methods only generate face images from random noise, and cannot generate face images according to the specific attributes. In this paper, we focus on the problem of face synthesis from attributes, which aims at generating faces with specific characteristics corresponding to the given attributes. To this end, we propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN. Specifically, we firstly propose a two-path embedding layer and self-attention mechanism to convert binary attribute vector to rich attribute features. Then three stacked generators generate 64 * 64, 128 * 128 and 256 * 256 resolution face images respectively by taking the attribute features as input. In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes. Extensive experiments on CelebA demonstrate the superiority of our AFGAN in terms of both qualitative and quantitative evaluations.

Quantifying the Use of Domain Randomization

Mohammad Ani, Hector Basevi, Ales Leonardis

Auto-TLDR; Evaluating Domain Randomization for Synthetic Image Generation by directly measuring the difference between realistic and synthetic data distributions

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Synthetic image generation provides the ability to efficiently produce large quantities of labeled data, which addresses both the data volume requirements of state-of-the-art vision systems and the expense of manually labeling data. However, systems trained on synthetic data typically under-perform systems trained on realistic data due to mismatch between the synthetic and realistic data distributions. Domain Randomization (DR) is a method of broadening a synthetic data distribution to encompass a realistic data distribution, and so provide better performance, when the exact characteristics of the realistic data distribution are not known or cannot be simulated. However, there is no consensus in the literature on the best method of performing DR. We propose a novel method of ranking DR methods by directly measuring the difference between realistic and DR data distributions. This avoids the need to measure task-specific performance and the associated expense of training and evaluation. We compare different methods for measuring distribution differences including the Wasserstein, and Fr\'echet Inception distances. We also examine the effect of performing this evaluation directly on images, and on features generated by an image classification backbone. Finally, we show that the ranking generated by our method is reflected in actual task performance.

S2I-Bird: Sound-To-Image Generation of Bird Species Using Generative Adversarial Networks

Joo Yong Shim, Joongheon Kim, Jong-Kook Kim

Auto-TLDR; Generating bird images from sound using conditional generative adversarial networks

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Generating images from sound is a challenging task. This paper proposes a novel deep learning model that generates bird images from their corresponding sound information. Our proposed model includes a sound encoder in order to extract suitable feature representations from audio recordings, and then it generates bird images that corresponds to its calls using conditional generative adversarial networks (GANs) with auxiliary classifiers. We demonstrate that our model produces better image generation results which outperforms other state-of-the-art methods in a similar context.

Continuous Learning of Face Attribute Synthesis

Ning Xin, Shaohui Xu, Fangzhe Nan, Xiaoli Dong, Weijun Li, Yuanzhou Yao

Auto-TLDR; Continuous Learning for Face Attribute Synthesis

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The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy.

Uncertainty-Aware Data Augmentation for Food Recognition

Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khatun, Marc Bolaños, Petia Radeva

Auto-TLDR; Data Augmentation for Food Recognition Using Epistemic Uncertainty

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Food recognition has recently attracted attention of many researchers. However, high food ambiguity, inter-class variability and intra-class similarity define a real challenge for the Deep learning and Computer Vision algorithms. In order to improve their performance, it is necessary to better understand what the model learns and, from this, to determine the type of data that should be additionally included for being the most beneficial to the training procedure. In this paper, we propose a new data augmentation strategy that estimates and uses the epistemic uncertainty to guide the model training. The method follows an active learning framework, where the new synthetic images are generated from the hard to classify real ones present in the training data based on the epistemic uncertainty. Hence, it allows the food recognition algorithm to focus on difficult images in order to learn their discriminatives features. On the other hand, avoiding data generation from images that do not contribute to the recognition makes it faster and more efficient. We show that the proposed method allows to improve food recognition and provides a better trade-off between micro- and macro-recall measures.

Learning Low-Shot Generative Networks for Cross-Domain Data

Hsuan-Kai Kao, Cheng-Che Lee, Wei-Chen Chiu

Auto-TLDR; Learning Generators for Cross-Domain Data under Low-Shot Learning

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We tackle a novel problem of learning generators for cross-domain data under a specific scenario of low-shot learning. Basically, given a source domain with sufficient amount of training data, we aim to transfer the knowledge of its generative process to another target domain, which not only has few data samples but also contains the domain shift with respect to the source domain. This problem has great potential in practical use and is different from the well-known image translation task, as the target-domain data can be generated without requiring any source-domain ones and the large data consumption for learning target-domain generator can be alleviated. Built upon a cross-domain dataset where (1) each of the low shots in the target domain has its correspondence in the source and (2) these two domains share the similar content information but different appearance, two approaches are proposed: a Latent-Disentanglement-Orientated model (LaDo) and a Generative-Hierarchy-Oriented (GenHo) model. Our LaDo and GenHo approaches address the problem from different perspectives, where the former relies on learning the disentangled representation composed of domain-invariant content features and domain-specific appearance ones; while the later decomposes the generative process of a generator into two parts for synthesizing the content and appearance sequentially. We perform extensive experiments under various settings of cross-domain data and show the efficacy of our models for generating target-domain data with the abundant content variance as in the source domain, which lead to the favourable performance in comparison to several baselines.

A Novel Computer-Aided Diagnostic System for Early Assessment of Hepatocellular Carcinoma

Ahmed Alksas, Mohamed Shehata, Gehad Saleh, Ahmed Shaffie, Ahmed Soliman, Mohammed Ghazal, Hadil Abukhalifeh, Abdel Razek Ahmed, Ayman El-Baz

Auto-TLDR; Classification of Liver Tumor Lesions from CE-MRI Using Structured Structural Features and Functional Features

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Early assessment of liver cancer patients with hepatocellular carcinoma (HCC) is of immense importance to provide the proper treatment plan. In this paper, we have developed a two-stage classification computer-aided diagnostic (CAD) system that has the ability to detect and grade the liver observations from multiphase contrast enhanced magnetic resonance imaging (CE-MRI). The proposed approach consists of three main steps. First, a pre-processing is applied to the CE-MRI scans to delineate the tumor lesions that will be used as an ROI across the four different phases of the CE-MRI, (namely, the pre-contrast, late-arterial, portal-venous, and delayed-contrast). Second, a group of three features are modeled to provide a quantitative discrimination between the tumor lesions; namely: i) the tumor appearance that is modeled using a set of texture features, (namely; the first-order histogram, second-order gray-level co-occurrence matrix, and second-order gray-level run-length matrix), to capture any discrimination that may appear in the lesion texture, ii) the spherical harmonics (SH) based shape features that have the ability to describe the shape complexity of the liver tumors, and iii) the functional features that are based on the calculation of the wash-in/wash-out through that evaluate the intensity changes across the post-contrast phases. Finally, the aforementioned individual features were then integrated together to obtain the combined features to be fed to a machine learning classifier towards getting the final diagnostic decision. The proposed CAD system has been tested using hepatic observations that was obtained from 85 participating patients, 34 patients with benign tumors, 34 patients with intermediate tumors and 34 with malignant tumors. Using a random forests based classifier with a leave-one-subject-out (LOSO) cross-validation, the developed CAD system achieved an 87.1% accuracy in distinguishing the malignant, intermediate and benign tumors. The classification performance is then evaluated using k-fold (5/10-fold) cross-validation approach to examine the robustness of the system. The LR-1 lesions were classified from LR-2 benign lesions with 91.2% accuracy, while 85.3% accuracy was achieved differentiating between LR-4 and LR-5 malignant tumors. The obtained results hold a promise of the proposed framework to be reliably used as a noninvasive diagnostic tool for the early detection and grading of liver cancer tumors.

GAN-Based Gaussian Mixture Model Responsibility Learning

Wanming Huang, Yi Da Xu, Shuai Jiang, Xuan Liang, Ian Oppermann

Auto-TLDR; Posterior Consistency Module for Gaussian Mixture Model

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Mixture Model (MM) is a probabilistic framework allows us to define dataset containing $K$ different modes. When each of the modes is associated with a Gaussian distribution, we refer to it as Gaussian MM or GMM. Given a data point $x$, a GMM may assume the existence of a random index $k \in \{1, \dots , K \}$ identifying which Gaussian the particular data is associated with. In a traditional GMM paradigm, it is straightforward to compute in closed-form, the conditional likelihood $p(x |k, \theta)$ as well as the responsibility probability $p(k|x, \theta)$ describing the distribution weights for each data. Computing the responsibility allows us to retrieve many important statistics of the overall dataset, including the weights of each of the modes/clusters. Modern large data-sets are often containing multiple unlabelled modes, such as paintings dataset may contain several styles; fashion images containing several unlabelled categories. In its raw representation, the Euclidean distances between the data (e.g., images) do not allow them to form mixtures naturally, nor it's feasible to compute responsibility distribution analytically, making GMM unable to apply. In this paper, we utilize the Generative Adversarial Network (GAN) framework to achieve a plausible alternative method to compute these probabilities. The key insight is that we compute them at the data's latent space $z$ instead of $x$. However, this process of $z \rightarrow x$ is irreversible under GAN which renders the computation of responsibility $p(k|x, \theta)$ infeasible. Our paper proposed a novel method to solve it by using a so-called Posterior Consistency Module (PCM). PCM acts like a GAN, except its Generator $C_{\text{PCM}}$ does not output the data, but instead it outputs a distribution to approximate $p(k|x, \theta)$. The entire network is trained in an ``end-to-end'' fashion. Trough these techniques, it allows us to model the dataset of very complex structure using GMM and subsequently to discover interesting properties of an unsupervised dataset, including its segments, as well as generating new ``out-distribution" data by smooth linear interpolation across any combinations of the modes in a completely unsupervised manner.

AVAE: Adversarial Variational Auto Encoder

Antoine Plumerault, Hervé Le Borgne, Celine Hudelot

Auto-TLDR; Combining VAE and GAN for Realistic Image Generation

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Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets.

Variational Capsule Encoder

Harish Raviprakash, Syed Anwar, Ulas Bagci

Auto-TLDR; Bayesian Capsule Networks for Representation Learning in latent space

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We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space. We hypothesize that this approach can learn a better representation of features in the latent space than traditional approaches. Our hypothesis was tested by using the learned latent variables for image reconstruction task, where for MNIST and Fashion-MNIST datasets, different classes were separated successfully in the latent space using our proposed model. Our experimental results have shown improved reconstruction and classification performances for both datasets adding credence to our hypothesis. We also showed that by increasing the latent space dimension, the proposed B-Caps was able to learn a better representation when compared to the traditional variational auto-encoders (VAE). Hence our results indicate the strength of capsule networks in representation learning which has never been examined under the VAE settings before.

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

Jingzhi Li, Lutong Han, Hua Zhang, Xiaoguang Han, Jingguo Ge, Xiaochu Cao

Auto-TLDR; Individual Face Privacy under Surveillance Scenario with Multi-task Loss Function

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In this paper, we focus on protecting the person face privacy under the surveillance scenarios, whose goal is to change the visual appearances of faces while keep them to be recognizable by current face recognition systems. This is a challenging problem as that we should retain the most important structures of captured facial images, while alter the salient facial regions to protect personal privacy. To address this problem, we introduce a novel individual face protection model, which can camouflage the face appearance from the perspective of human visual perception and preserve the identity features of faces used for face authentication. To that end, we develop an encoder-decoder network architecture that can separately disentangle the person feature representation into an appearance code and an identity code. Specifically, we first randomly divide the face image into two groups, the source set and the target set, where the source set is used to extract the identity code and the target set provides the appearance code. Then, we recombine the identity and appearance codes to synthesize a new face, which has the same identity with the source subject. Finally, the synthesized faces are used to replace the original face to protect the privacy of individual. Furthermore, our model is trained end-to-end with a multi-task loss function, which can better preserve the identity and stabilize the training loss. Experiments conducted on Cross-Age Celebrity dataset demonstrate the effectiveness of our model and validate our superiority in terms of visual quality and scalability.

An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks

Debapriya Roy, Diganta Mukherjee, Bhabatosh Chanda

Auto-TLDR; Unsupervised Skin Tone Change Using Augmented Reality Based Models

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With the increasing popularity of augmented and virtual reality, retailers are now more focusing towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained its much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image. This is an unsupervised method and also unconstrained in terms of pose, illumination, number of persons in the image etc. The goal of this work is to reduce the complexity in terms of time and effort which is generally needed for changing the skin tone using existing applications by professionals or novice. Rigorous experiments shows the efficacy of this method in terms of synthesizing perceptually convincing outputs.

Generating Private Data Surrogates for Vision Related Tasks

Ryan Webster, Julien Rabin, Loic Simon, Frederic Jurie

Auto-TLDR; Generative Adversarial Networks for Membership Inference Attacks

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With the widespread application of deep networks in industry, membership inference attacks, i.e. the ability to discern training data from a model, become more and more problematic for data privacy. Recent work suggests that generative networks may be robust against membership attacks. In this work, we build on this observation, offering a general-purpose solution to the membership privacy problem. As the primary contribution, we demonstrate how to construct surrogate datasets, using images from GAN generators, labelled with a classifier trained on the private dataset. Next, we show this surrogate data can further be used for a variety of downstream tasks (here classification and regression), while being resistant to membership attacks. We study a variety of different GANs proposed in the literature, concluding that higher quality GANs result in better surrogate data with respect to the task at hand.

Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification

Federico Pollastri, Juan Maroñas, Federico Bolelli, Giulia Ligabue, Roberto Paredes, Riccardo Magistroni, Costantino Grana

Auto-TLDR; A Probabilistic Convolutional Neural Network for Immunofluorescence Classification in Renal Biopsy

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With this work we tackle immunofluorescence classification in renal biopsy, employing state-of-the-art Convolutional Neural Networks. In this setting, the aim of the probabilistic model is to assist an expert practitioner towards identifying the location pattern of antibody deposits within a glomerulus. Since modern neural networks often provide overconfident outputs, we stress the importance of having a reliable prediction, demonstrating that Temperature Scaling, a recently introduced re-calibration technique, can be successfully applied to immunofluorescence classification in renal biopsy. Experimental results demonstrate that the designed model yields good accuracy on the specific task, and that Temperature Scaling is able to provide reliable probabilities, which are highly valuable for such a task given the low inter-rater agreement.

A Multi-Task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation

Ngan Le, Kashu Yamazaki, Quach Kha Gia, Thanh-Dat Truong, Marios Savvides

Auto-TLDR; Contextual Brain Tumor Segmentation Using 3D atrous Residual Networks and Cascaded Structures

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In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segmentation. We investigate that segmenting brain tumor is facing to the imbalanced data problem where the number of pixels belonging to background class (non tumor pixel) is much larger than the number of pixels belonging to foreground class (tumor pixel). To address this problem, we propose a multi-task network which is formed as a cascaded structure and designed to share the feature maps. Our model consists of two targets, i.e., (i) effectively differentiating brain tumor regions and (ii) estimating brain tumor masks. The first task is performed by our proposed contextual brain tumor detection network, which plays the role of an attention gate and focuses on the region around brain tumor only while ignore the background (non tumor area). Instead of processing every pixel, our contextual brain tumor detection network only processes contextual regions around ground-truth instances and this strategy helps to produce meaningful regions proposals. The second task is built upon a 3D atrous residual network and under an encode-decode network in order to effectively segment both large and small objects (brain tumor). Our 3D atrous residual network is designed with a skip connection to enables the gradient from the deep layers to be directly propagated to shallow layers, thus, features of different depths are preserved and used for refining each other. In order to incorporate larger contextual information in volume MRI data, our network is designed by 3D atrous convolution with various kernel sizes, which enlarges the receptive field of filters. Our proposed network has been evaluated on various datasets including BRATS2015, BRATS2017 and BRATS2018 datasets with both validation set and testing set. Our performance has been benchmarked by both region-based metrics and surface-based metrics. We also have conducted comparisons against state-of-the-art approaches.

High Resolution Face Age Editing

Xu Yao, Gilles Puy, Alasdair Newson, Yann Gousseau, Pierre Hellier

Auto-TLDR; An Encoder-Decoder Architecture for Face Age editing on High Resolution Images

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Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, current methods often present visual artifacts and can only deal with low-resolution images. In order to achieve aging/de-aging with the high quality and robustness necessary for wider use, these problems need to be addressed. This is the goal of the present work. We present an encoder-decoder architecture for face age editing. The core idea of our network is to encode a face image to age-invariant features, and learn a modulation vector corresponding to a target age. We then combine these two elements to produce a realistic image of the person with the desired target age. Our architecture is greatly simplified with respect to other approaches, and allows for fine-grained age editing on high resolution images in a single unified model. Source codes are available at https://github.com/InterDigitalInc/HRFAE.

A Comparison of Neural Network Approaches for Melanoma Classification

Maria Frasca, Michele Nappi, Michele Risi, Genoveffa Tortora, Alessia Auriemma Citarella

Auto-TLDR; Classification of Melanoma Using Deep Neural Network Methodologies

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Melanoma is the deadliest form of skin cancer and it is diagnosed mainly visually, starting from initial clinical screening and followed by dermoscopic analysis, biopsy and histopathological examination. A dermatologist’s recognition of melanoma may be subject to errors and may take some time to diagnose it. In this regard, deep learning can be useful in the study and classification of skin cancer. In particular, by classifying images with Deep Neural Network methodologies, it is possible to obtain comparable or even superior results compared to those of dermatologists. In this paper, we propose a methodology for the classification of melanoma by adopting different deep learning techniques applied to a common dataset, composed of images from the ISIC dataset and consisting of different types of skin diseases, including melanoma on which we applied a specific pre-processing phase. In particular, a comparison of the results is performed in order to select the best effective neural network to be applied to the problem of recognition and classification of melanoma. Moreover, we also evaluate the impact of the pre- processing phase on the final classification. Different metrics such as accuracy, sensitivity, and specificity have been selected to assess the goodness of the adopted neural networks and compare them also with the manual classification of dermatologists.

MTGAN: Mask and Texture-Driven Generative Adversarial Network for Lung Nodule Segmentation

Wei Chen, Qiuli Wang, Kun Wang, Dan Yang, Xiaohong Zhang, Chen Liu, Yucong Li

Auto-TLDR; Mask and Texture-driven Generative Adversarial Network for Lung Nodule Segmentation

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Accurate segmentation for lung nodules in lung computed tomography (CT) scans plays a key role in the early diagnosis of lung cancer. Many existing methods, especially UNet, have made significant progress in lung nodule segmentation. However, due to the complex shapes of lung nodules and the similarity of visual characteristics between nodules and lung tissues, an accurate segmentation with few false positives of lung nodules is still a challenging problem. Considering the fact that both boundary and texture information of lung nodules are important for obtaining an accurate segmentation result, we propose a novel Mask and Texture-driven Generative Adversarial Network (MTGAN) with a joint multi-scale L1 loss for lung nodule segmentation, which takes full advantages of U-Net and adversarial training. The proposed MTGAN leverages adversarial learning strategy guided by the boundary and texture information of lung nodules to generate more accurate segmentation results with lesser false positives. We validate our model with the LIDC–IDRI dataset, and experimental results show that our method achieves excellent segmentation results for a variety of lung nodules, especially for juxtapleural nodules and low-dense nodules. Without any bells and whistles, the proposed MTGAN achieves significant segmentation performance with the Dice similarity coefficient (DSC) of 85.24% on the LIDC–IDRI dataset.

Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise

Anne-Sophie Collin, Christophe De Vleeschouwer

Auto-TLDR; Autoencoder with Skip Connections for Anomaly Detection

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In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. We show that this model favors the reconstruction of clean images from arbitrary real-world images, regardless of the actual defects appearance. In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset [ref.], both for pixel- and image-wise anomaly detection.

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

Stefano Allegretti, Federico Bolelli, Federico Pollastri, Sabrina Longhitano, Giovanni Pellacani, Costantino Grana

Auto-TLDR; Skin Images Retrieval Using Convolutional Neural Networks for Skin Lesion Classification and Segmentation

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Given the relevance of skin cancer, many attempts have been dedicated to the creation of automated devices that could assist both expert and beginner dermatologists towards fast and early diagnosis of skin lesions. In recent years, tasks such as skin lesion classification and segmentation have been extensively addressed with deep learning algorithms, which in some cases reach a diagnostic accuracy comparable to that of expert physicians. However, the general lack of interpretability and reliability severely hinders the ability of those approaches to actually support dermatologists in the diagnosis process. In this paper a novel skin images retrieval system is presented, which exploits features extracted by Convolutional Neural Networks to gather similar images from a publicly available dataset, in order to assist the diagnosis process of both expert and novice practitioners. In the proposed framework, Resnet-50 is initially trained for the classification of dermoscopic images; then, the feature extraction part is isolated, and an embedding network is build on top of it. The embedding learns an alternative representation, which allows to check image similarity by means of a distance measure. Experimental results reveal that the proposed method is able to select meaningful images, which can effectively boost the classification accuracy of human dermatologists.

Vesselness Filters: A Survey with Benchmarks Applied to Liver Imaging

Jonas Lamy, Odyssée Merveille, Bertrand Kerautret, Nicolas Passat, Antoine Vacavant

Auto-TLDR; Comparison of Vessel Enhancement Filters for Liver Vascular Network Segmentation

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The accurate knowledge of vascular network geometry is crucial for many clinical applications such as cardiovascular disease diagnosis and surgery planning. Vessel enhancement algorithms are often a key step to improve the robustness of vessel segmentation. A wide variety of enhancement filters exists in the literature, but they are often difficult to compare as the applications and datasets differ from a paper to another and the code is rarely available. In this article, we compare seven vessel enhancement filters covering the last twenty years literature in a unique common framework. We focus our study on the liver vascular network which is under-represented in the literature. The evaluation is made from three points of view: in the whole liver, in the vessel neighborhood and near the bifurcations. The study is performed on two publicly available datasets: the Ircad dataset (CT images) and the VascuSynth dataset adapted for MRI simulation. We discuss the strengths and weaknesses of each method in the hepatic context. In addition, the benchmark framework including a C++ implementation of each compared method is provided. An online demonstration ensures the reproducibility of the results without requiring any additional software.

Age Gap Reducer-GAN for Recognizing Age-Separated Faces

Daksha Yadav, Naman Kohli, Mayank Vatsa, Richa Singh, Afzel Noore

Auto-TLDR; Generative Adversarial Network for Age-separated Face Recognition

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In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework which combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.

Tarsier: Evolving Noise Injection inSuper-Resolution GANs

Baptiste Roziere, Nathanaël Carraz Rakotonirina, Vlad Hosu, Rasoanaivo Andry, Hanhe Lin, Camille Couprie, Olivier Teytaud

Auto-TLDR; Evolutionary Super-Resolution using Diagonal CMA

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Super-resolution aims at increasing the resolution and level of detail within an image. The current state of the art in general single-image super-resolution is held by nESRGAN+,which injects a Gaussian noise after each residual layer at training time. In this paper, we harness evolutionary methods to improve nESRGAN+ by optimizing the noise injection at inference time. More precisely, we use Diagonal CMA to optimize the injected noise according to a novel criterion combining quality assessment and realism. Our results are validated by the PIRM perceptual score and a human study. Our method outperforms nESRGAN+ on several standard super-resolution datasets. More generally, our approach can be used to optimize any method based on noise injection.

A NoGAN Approach for Image and Video Restoration and Compression Artifact Removal

Mameli Filippo, Marco Bertini, Leonardo Galteri, Alberto Del Bimbo

Auto-TLDR; Deep Neural Network for Image and Video Compression Artifact Removal and Restoration

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Lossy image and video compression algorithms introduce several different types of visual artifacts that reduce the visual quality of the compressed media, and the higher the compression rate the higher is the strength of these artifacts. In this work, we describe an approach for visual quality improvement of compressed images and videos to be performed at presentation time, so to obtain the benefits of fast data transfer and reduced data storage, while enjoying a visual quality that could be obtained only reducing the compression rate. To obtain this result we propose to use a deep neural network trained using the NoGAN approach, adapting the popular DeOldify architecture used for colorization. We show how the proposed method can be applied both to image and video compression artifact removal and restoration.

Phase Retrieval Using Conditional Generative Adversarial Networks

Tobias Uelwer, Alexander Oberstraß, Stefan Harmeling

Auto-TLDR; Conditional Generative Adversarial Networks for Phase Retrieval

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In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurement process at training time leads to an optimization at test time that is more robust to initialization than existing approaches involving generative models. In addition, conditioning the generator network on the measurements enables us to achieve much more detailed results. We empirically demonstrate that these advantages provide meaningful solutions to the Fourier and the compressive phase retrieval problem and that our method outperforms well-established projection-based methods as well as existing methods that are based on neural networks. Like other deep learning methods, our approach is very robust to noise and can therefore be very useful for real-world applications.

Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images Using Generative Adversarial Networks

Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod

Auto-TLDR; Fluorescein Angiography from Fundus Images using Attention-based Generative Networks

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Fluorescein Angiography (FA) is a technique that employs the designated camera for Fundus photography incorporating excitation and barrier filters. FA also requires fluorescein dye that is injected intravenously, which might cause adverse effects ranging from nausea, vomiting to even fatal anaphylaxis. Currently, no other fast and non-invasive technique exists that can generate FA without coupling with Fundus photography. To eradicate the need for an invasive FA extraction procedure, we introduce an Attention-based Generative network that can synthesize Fluorescein Angiography from Fundus images. The proposed gan incorporates multiple attention based skip connections in generators and comprises novel residual blocks for both generators and discriminators. It utilizes reconstruction, feature-matching, and perceptual loss along with adversarial training to produces realistic Angiograms that is hard for experts to distinguish from real ones. Our experiments confirm that the proposed architecture surpasses recent state-of-the-art generative networks for fundus-to-angio translation task.

Pseudo Rehearsal Using Non Photo-Realistic Images

Bhasker Sri Harsha Suri, Kalidas Yeturu

Auto-TLDR; Pseudo-Rehearsing for Catastrophic Forgetting

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Deep Neural networks forget previously learnt tasks when they are faced with learning new tasks. This is called catastrophic forgetting. Rehearsing the neural network with the training data of the previous task can protect the network from catastrophic forgetting.Since rehearsing requires the storage of entire previous data, Pseudo rehearsal was proposed, where samples belonging to the previous data are generated synthetically for rehearsal. In an image classification setting, while current techniques try to generate synthetic data that is photo-realistic, we demonstrated that Neural networks can be rehearsed on data that is not photo-realistic and still achieve good retention of the previous task. We also demonstrated that forgoing the constraint of having photo realism in the generated data can result in a significant reduction in the consumption of computational and memory resources for pseudo rehearsal.

Evaluation of Anomaly Detection Algorithms for the Real-World Applications

Marija Ivanovska, Domen Tabernik, Danijel Skocaj, Janez Pers

Auto-TLDR; Evaluating Anomaly Detection Algorithms for Practical Applications

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Anomaly detection in complex data structures is oneof the most challenging problems in computer vision. In manyreal-world problems, for example in the quality control in modernmanufacturing, the anomalous samples are usually rare, resultingin (highly) imbalanced datasets. However, in current researchpractice, these scenarios are rarely modeled, and as a conse-quence, evaluation of anomaly detection algorithms often do notreproduce results that are useful for practical applications. First,even in case of highly unbalanced input data, anomaly detectionalgorithms are expected to significantly reduce the proportionof anomalous samples, detecting ”almost all” anomalous samples(with exact specifications depending on the target customer). Thisplaces high importance on only the small part of the ROC curve,possibly rendering the standard metrics such as AUC (AreaUnder Curve) and AP (Average Precision) useless. Second, thetarget of automatic anomaly detection in practical applicationsis significant reduction in manual work required, and standardmetrics are poor predictor of this feature. Finally, the evaluationmay produce erratic results for different randomly initializedtraining runs of the neural network, producing evaluation resultsthat may not reproduce well in practice. In this paper, we presentan evaluation methodology that avoids these pitfalls.

Dual Stream Network with Selective Optimization for Skin Disease Recognition in Consumer Grade Images

Krishnam Gupta, Jaiprasad Rampure, Monu Krishnan, Ajit Narayanan, Nikhil Narayan

Auto-TLDR; A Deep Network Architecture for Skin Disease Localisation and Classification on Consumer Grade Images

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Skin disease localisation and classification on consumer-grade images is more challenging compared to that on dermoscopic imaging. Consumer grade images refer to the images taken using commonly available imaging devices such as a mobile camera or a hand held digital camera. Such images, in addition to having the skin condition of interest in a very small area of the image, has other noisy non-clinical details introduced due to the lighting conditions and the distance of the hand held device from the anatomy at the time of acquisition. We propose a novel deep network architecture \& a new optimization strategy for classification with implicit localisation of skin diseases from clinical/consumer grade images. A weakly supervised segmentation algorithm is first employed to extract Region of Interests (RoI) from the image, the RoI and the original image form the two input streams of the proposed architecture. Each stream of the architecture learns high level and low level features from the original image and the RoI, respectively. The two streams are independently optimised until the loss stops decreasing after which both the streams are optimised collectively with the help of a third combiner sub-network. Such a strategy resulted in a 5% increase of accuracy over the current state-of-the-art methods on SD-198 dataset, which is publicly available. The proposed algorithm is also validated on a new dataset containing over 12,000 images across 75 different skin conditions. We intend to release this dataset as SD-75 to aid in the advancement of research on skin condition classification on consumer grade images.

Data Augmentation Via Mixed Class Interpolation Using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery

Hiroshi Sasaki, Chris G. Willcocks, Toby Breckon

Auto-TLDR; C2GMA: A Generative Domain Transfer Model for Non-visible Domain Classification

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Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches. To address this problem, this paper proposes and evaluates a novel data augmentation approach that leverages the more readily available visible-band imagery via a generative domain transfer model. The model can synthesise large volumes of non-visible domain imagery by image-to-image (I2I) translation from the visible image domain. Furthermore, we show that the generation of interpolated mixed class (non-visible domain) image examples via our novel Conditional CycleGAN Mixup Augmentation (C2GMA) methodology can lead to a significant improvement in the quality of non-visible domain classification tasks that otherwise suffer due to limited data availability. Focusing on classification within the Synthetic Aperture Radar (SAR) domain, our approach is evaluated on a variation of the Statoil/C-CORE Iceberg Classifier Challenge dataset and achieves 75.4% accuracy, demonstrating a significant improvement when compared against traditional data augmentation strategies (Rotation, Mixup, and MixCycleGAN).