Cross-View Relation Networks for Mammogram Mass Detection

Ma Jiechao, Xiang Li, Hongwei Li, Ruixuan Wang, Bjoern Menze, Wei-Shi Zheng

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Auto-TLDR; Multi-view Modeling for Mass Detection in Mammogram

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In medical image analysis, multi-view modeling is crucial for pathology detection when the target lesion is presented in different views, e.g. mass lesions in breast. Currently mammogram is the most effective imaging modality for mass lesion detection of breast cancer at the early stage. The pathological information from the two paired views (i.e., medio-lateral oblique and cranio-caudal) are highly relational and complementary, which is crucial for diagnosis in clinical practice. Existing mass detection methods do not consider learning synergistic features from the two relational views. For the first time, we propose a novel mass detection framework to capture the latent relation information from the two paired views of a same mass in mammogram. We evaluate our model on a public mammogram dataset and a large-scale private dataset, demonstrating that the proposed method outperforms existing feature fusion approaches and state-of-the-art mass detection methods. We further analyze the performance gains from the relation modeling. Our quantitative and qualitative results suggest that jointly learning cross-view features boosts the detection performance of existing models, which is a promising avenue for mass detection task in mammogram.

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Jiaqi Luo, Zhicheng Zhao, Fei Su, Limei Guo

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Auto-TLDR; Triplet-path Network for One-Stage Object Detection and Segmentation in Pathological Images

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Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.

A Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification

Pierluigi Carcagni, Marco Leo, Andrea Cuna, Giuseppe Celeste, Cosimo Distante

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Auto-TLDR; RegNet: Deep Investigation of Convolutional Neural Networks for Automatic Classification of Skin Lesions

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Small Object Detection by Generative and Discriminative Learning

Yi Gu, Jie Li, Chentao Wu, Weijia Jia, Jianping Chen

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Auto-TLDR; Generative and Discriminative Learning for Small Object Detection

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With the development of deep convolutional neural networks (CNNs), the object detection accuracy has been greatly improved. But the performance of small object detection is still far from satisfactory, mainly because small objects are so tiny that the information contained in the feature map is limited. Existing methods focus on improving classification accuracy but still suffer from the limitation of bounding box prediction. To solve this issue, we propose a detection framework by generative and discriminative learning. First, a reconstruction generator network is designed to reconstruct the mapping from low frequency to high frequency for anchor box prediction. Then, a detector module extracts the regions of interest (ROIs) from generated results and implements a RoI-Head to predict object category and refine bounding box. In order to guide the reconstructed image related to the corresponding one, a discriminator module is adopted to tell from the generated result and the original image. Extensive evaluations on the challenging MS-COCO dataset demonstrate that our model outperforms most state-of-the-art models in detecting small objects, especially the reconstruction module improves the average precision for small object (APs) by 7.7%.

Attention Based Multi-Instance Thyroid Cytopathological Diagnosis with Multi-Scale Feature Fusion

Shuhao Qiu, Yao Guo, Chuang Zhu, Wenli Zhou, Huang Chen

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Auto-TLDR; A weakly supervised multi-instance learning framework based on attention mechanism with multi-scale feature fusion for thyroid cytopathological diagnosis

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Learning a Dynamic High-Resolution Network for Multi-Scale Pedestrian Detection

Mengyuan Ding, Shanshan Zhang, Jian Yang

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Auto-TLDR; Learningable Dynamic HRNet for Pedestrian Detection

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Pedestrian detection is a canonical instance of object detection in computer vision. In practice, scale variation is one of the key challenges, resulting in unbalanced performance across different scales. Recently, the High-Resolution Network (HRNet) has become popular because high-resolution feature representations are more friendly to small objects. However, when we apply HRNet for pedestrian detection, we observe that it improves for small pedestrians on one hand, but hurts the performance for larger ones on the other hand. To overcome this problem, we propose a learnable Dynamic HRNet (DHRNet) aiming to generate different network paths adaptive to different scales. Specifically, we construct a parallel multi-branch architecture and add a soft conditional gate module allowing for dynamic feature fusion. Both branches share all the same parameters except the soft gate module. Experimental results on CityPersons and Caltech benchmarks indicate that our proposed dynamic HRNet is more capable of dealing with pedestrians of various scales, and thus improves the performance across different scales consistently.

SFPN: Semantic Feature Pyramid Network for Object Detection

Yi Gan, Wei Xu, Jianbo Su

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Auto-TLDR; SFPN: Semantic Feature Pyramid Network to Address Information Dilution Issue in FPN

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Classify Breast Histopathology Images with Ductal Instance-Oriented Pipeline

Beibin Li, Ezgi Mercan, Sachin Mehta, Stevan Knezevich, Corey Arnold, Donald Weaver, Joann Elmore, Linda Shapiro

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Auto-TLDR; DIOP: Ductal Instance-Oriented Pipeline for Diagnostic Classification

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Object Detection Using Dual Graph Network

Shengjia Chen, Zhixin Li, Feicheng Huang, Canlong Zhang, Huifang Ma

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Auto-TLDR; A Graph Convolutional Network for Object Detection with Key Relation Information

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Detecting Objects with High Object Region Percentage

Fen Fang, Qianli Xu, Liyuan Li, Ying Gu, Joo-Hwee Lim

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Auto-TLDR; Faster R-CNN for High-ORP Object Detection

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Object shape is a subtle but important factor for object detection. It has been observed that the object-region-percentage (ORP) can be utilized to improve detection accuracy for elongated objects, which have much lower ORPs than other types of objects. In this paper, we propose an approach to improve the detection performance for objects whose ORPs are relatively higher.To address the problem of high-ORP object detection, we propose a method consisting of three steps. First, we adjust the ground truth bounding boxes of high-ORP objects to an optimal range. Second, we train an object detector, Faster R-CNN, based on adjusted bounding boxes to achieve high recall. Finally, we train a DCNN to learn the adjustment ratios towards four directions and adjust detected bounding boxes of objects to get better localization for higher precision. We evaluate the effectiveness of our method on 12 high-ORP objects in COCO and 8 objects in a proprietary gearbox dataset. The experimental results show that our method can achieve state-of-the-art performance on these objects while costing less resources in training and inference stages.

Object Detection Model Based on Scene-Level Region Proposal Self-Attention

Yu Quan, Zhixin Li, Canlong Zhang, Huifang Ma

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Auto-TLDR; Exploiting Semantic Informations for Object Detection

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End-To-End Multi-Task Learning for Lung Nodule Segmentation and Diagnosis

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

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Auto-TLDR; A novel multi-task framework for lung nodule diagnosis based on deep learning and medical features

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Computer-Aided Diagnosis (CAD) systems for lung nodule diagnosis based on deep learning have attracted much attention in recent years. However, most existing methods ignore the relationships between the segmentation and classification tasks, which leads to unstable performances. To address this problem, we propose a novel multi-task framework, which can provide lung nodule segmentation mask, malignancy prediction, and medical features for interpretable diagnosis at the same time. Our framework mainly contains two sub-network: (1) Multi-Channel Segmentation Sub-network (MSN) for lung nodule segmentation, and (2) Joint Classification Sub-network (JCN) for interpretable lung nodule diagnosis. In the proposed framework, we use U-Net down-sampling processes for extracting low-level deep learning features, which are shared by two sub-networks. The JCN forces the down-sampling processes to learn better lowlevel deep features, which lead to a better construct of segmentation masks. Meanwhile, two additional channels constructed by OTSU and super-pixel (SLIC) methods, are utilized as the guideline of the feature extraction. The proposed framework takes advantages of deep learning methods and classical methods, which can significantly improve the performances of all tasks. We evaluate the proposed framework on public dataset LIDCIDRI. Our framework achieves a promising Dice score of 86.43% in segmentation, 87.07% in malignancy level prediction, and convincing results in interpretable medical feature predictions.

CAggNet: Crossing Aggregation Network for Medical Image Segmentation

Xu Cao, Yanghao Lin

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Auto-TLDR; Crossing Aggregation Network for Medical Image Segmentation

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In this paper, we present Crossing Aggregation Network (CAggNet), a novel densely connected semantic segmentation method for medical image analysis. The crossing aggregation network absorbs the idea of deep layer aggregation and makes significant innovations in layer connection and semantic information fusion. In this architecture, the traditional skip-connection structure of general U-Net is replaced by aggregations of multi-level down-sampling and up-sampling layers. This enables the network to fuse information interactively flows at different levels of layers in semantic segmentation. It also introduces weighted aggregation module to aggregate multi-scale output information. We have evaluated and compared our CAggNet with several advanced U-Net based methods in two public medical image datasets, including the 2018 Data Science Bowl nuclei detection dataset and the 2015 MICCAI gland segmentation competition dataset. Experimental results indicate that CAggNet improves medical object recognition and achieves a more accurate and efficient segmentation compared to existing improved U-Net and UNet++ structure.

One-Stage Multi-Task Detector for 3D Cardiac MR Imaging

Weizeng Lu, Xi Jia, Wei Chen, Nicolò Savioli, Antonio De Marvao, Linlin Shen, Declan O'Regan, Jinming Duan

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Auto-TLDR; Multi-task Learning for Real-Time, simultaneous landmark location and bounding box detection in 3D space

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Fast and accurate landmark location and bounding box detection are important steps in 3D medical imaging. In this paper, we propose a novel multi-task learning framework, for real-time, simultaneous landmark location and bounding box detection in 3D space. Our method extends the famous single-shot multibox detector (SSD) from single-task learning to multi-task learning and from 2D to 3D. Furthermore, we propose a post-processing approach to refine the network landmark output, by averaging the candidate landmarks. Owing to these settings, the proposed framework is fast and accurate. For 3D cardiac magnetic resonance (MR) images with size 224 × 224 × 64, our framework runs about 128 volumes per second (VPS) on GPU and achieves 6.75mm average point-to-point distance error for landmark location, which outperforms both state-of-the-art and baseline methods. We also show that segmenting the 3D image cropped with the bounding box results in both improved performance and efficiency.

Learn to Segment Retinal Lesions and Beyond

Qijie Wei, Xirong Li, Weihong Yu, Xiao Zhang, Yongpeng Zhang, Bojie Hu, Bin Mo, Di Gong, Ning Chen, Dayong Ding, Youxin Chen

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Auto-TLDR; Multi-task Lesion Segmentation and Disease Classification for Diabetic Retinopathy Grading

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Towards automated retinal screening, this paper makes an endeavor to simultaneously achieve pixel-level retinal lesion segmentation and image-level disease classification. Such a multi-task approach is crucial for accurate and clinically interpretable disease diagnosis. Prior art is insufficient due to three challenges, i.e., lesions lacking objective boundaries, clinical importance of lesions irrelevant to their size, and the lack of one-to-one correspondence between lesion and disease classes. This paper attacks the three challenges in the context of diabetic retinopathy (DR) grading. We propose Lesion-Net, a new variant of fully convolutional networks, with its expansive path re- designed to tackle the first challenge. A dual Dice loss that leverages both semantic segmentation and image classification losses is introduced to resolve the second challenge. Lastly, we build a multi-task network that employs Lesion-Net as a side- attention branch for both DR grading and result interpretation. A set of 12K fundus images is manually segmented by 45 ophthalmologists for 8 DR-related lesions, resulting in 290K manual segments in total. Extensive experiments on this large- scale dataset show that our proposed approach surpasses the prior art for multiple tasks including lesion segmentation, lesion classification and DR grading.

DA-RefineNet: Dual-Inputs Attention RefineNet for Whole Slide Image Segmentation

Ziqiang Li, Rentuo Tao, Qianrun Wu, Bin Li

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Auto-TLDR; DA-RefineNet: A dual-inputs attention network for whole slide image segmentation

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Automatic medical image segmentation techniques have wide applications for disease diagnosing, however, its much more challenging than natural optical image segmentation tasks due to the high-resolution of medical images and the corresponding huge computation cost. Sliding window was a commonly used technique for whole slide image (WSI) segmentation, however, for these methods that based on sliding window, the main drawback was lacking of global contextual information for supervision. In this paper, we proposed a dual-inputs attention network (denoted as DA-RefineNet) for WSI segmentation, where both local fine-grained information and global coarse information can be efficiently utilized. Sufficient comparative experiments were conducted to evaluate the effectiveness of the proposed method, the results proved that the proposed method can achieve better performance on WSI segmentation tasks compared to methods rely on single-input.

Improving Visual Relation Detection Using Depth Maps

Sahand Sharifzadeh, Sina Moayed Baharlou, Max Berrendorf, Rajat Koner, Volker Tresp

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Auto-TLDR; Exploiting Depth Maps for Visual Relation Detection

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State-of-the-art visual relation detection methods mostly rely on object information extracted from RGB images such as 2D bounding boxes, feature maps, and predicted class probabilities. Depth maps can additionally provide valuable information on object relations, e.g. helping to detect not only spatial relations, such as standing behind, but also non-spatial relations, such as holding. In this work, we study the effect of using different object information with a focus on depth maps. To enable this study, we release a new synthetic dataset of depth maps, VG-Depth, as an extension to Visual Genome (VG). We also note that given the highly imbalanced distribution of relations in VG, typical evaluation metrics for visual relation detection cannot reveal improvements of under-represented relations. To address this problem, we propose using an additional metric, calling it Macro Recall@K, and demonstrate its remarkable performance on VG. Finally, our experiments confirm that by effective utilization of depth maps within a simple, yet competitive framework, the performance of visual relation detection can be improved by a margin of up to 8%.

Forground-Guided Vehicle Perception Framework

Kun Tian, Tong Zhou, Shiming Xiang, Chunhong Pan

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Auto-TLDR; A foreground segmentation branch for vehicle detection

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

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

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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.

Adaptive Word Embedding Module for Semantic Reasoning in Large-Scale Detection

Yu Zhang, Xiaoyu Wu, Ruolin Zhu

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Auto-TLDR; Adaptive Word Embedding Module for Object Detection

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Using Scene Graphs for Detecting Visual Relationships

Anurag Tripathi, Siddharth Srivastava, Brejesh Lall, Santanu Chaudhury

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Auto-TLDR; Relationship Detection using Context Aligned Scene Graph Embeddings

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In this paper we solve the problem of detecting relationships between pairs of objects in an image. We develop spatially aware word embeddings using scene graphs and use joint feature representations containing visual, spatial and semantic embeddings from the input images to train a deep network on the task of relationship detection. Further, we propose to utilize context aligned scene graph embeddings from the train set, without requiring explicit availability of scene graphs at test time. We show that the proposed method outperforms the state-of-the-art methods for predicate detection and provides competing results on relationship detection. We also show the generalization ability of the proposed method by performing predictions under zero shot settings. Further, we also provide an exhaustive empirical evaluation on each component of the proposed network.

Construction Worker Hardhat-Wearing Detection Based on an Improved BiFPN

Chenyang Zhang, Zhiqiang Tian, Jingyi Song, Yaoyue Zheng, Bo Xu

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Auto-TLDR; A One-Stage Object Detection Method for Hardhat-Wearing in Construction Site

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Work in the construction site is considered to be one of the occupations with the highest safety risk factor. Therefore, safety plays an important role in construction site. One of the most fundamental safety rules in construction site is to wear a hardhat. To strengthen the safety of the construction site, most of the current methods use multi-stage method for hardhat-wearing detection. These methods have limitations in terms of adaptability and generalizability. In this paper, we propose a one-stage object detection method based on convolutional neural network. We present a multi-scale strategy that selects the high-resolution feature maps of DarkNet-53 to effectively identify small-scale hardhats. In addition, we propose an improved weighted bi-directional feature pyramid network (BiFPN), which could fuse more semantic features from more scales. The proposed method can not only detect hardhat-wearing, but also identify the color of the hardhat. Experimental results show that the proposed method achieves a mAP of 87.04%, which outperforms several state-of-the-art methods on a public dataset.

A Novel Region of Interest Extraction Layer for Instance Segmentation

Leonardo Rossi, Akbar Karimi, Andrea Prati

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Auto-TLDR; Generic RoI Extractor for Two-Stage Neural Network for Instance Segmentation

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Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extract a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome to the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP on bounding box detection and 1.7% AP on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection-groie

Scene Text Detection with Selected Anchors

Anna Zhu, Hang Du, Shengwu Xiong

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Auto-TLDR; AS-RPN: Anchor Selection-based Region Proposal Network for Scene Text Detection

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Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal network (AS-RPN) using effective selected anchors instead of dense anchors to extract text proposals. The center, scales, aspect ratios and orientations of anchors are learnable instead of fixing, which leads to high recall and greatly reduced numbers of anchors. By replacing the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve comparable performance with previous state-of-the-art text detecting approaches on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and MSRA-TD500 when using single-scale and single model (ResNet50) testing only.

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

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

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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.

Efficient-Receptive Field Block with Group Spatial Attention Mechanism for Object Detection

Jiacheng Zhang, Zhicheng Zhao, Fei Su

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Auto-TLDR; E-RFB: Efficient-Receptive Field Block for Deep Neural Network for Object Detection

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Object detection has been paid rising attention in computer vision field. Convolutional Neural Networks (CNNs) extract high-level semantic features of images, which directly determine the performance of object detection. As a common solution, embedding integration modules into CNNs can enrich extracted features and thereby improve the performance. However, the instability and inconsistency of internal multiple branches exist in these modules. To address this problem, we propose a novel multibranch module called Efficient-Receptive Field Block (E-RFB), in which multiple levels of features are combined for network optimization. Specifically, by downsampling and increasing depth, the E-RFB provides sufficient RF. Second, in order to eliminate the inconsistency across different branches, a novel spatial attention mechanism, namely, Group Spatial Attention Module (GSAM) is proposed. The GSAM gradually narrows a feature map by channel grouping; thus it encodes the information between spatial and channel dimensions into the final attention heat map. Third, the proposed module can be easily joined in various CNNs to enhance feature representation as a plug-and-play component. With SSD-style detectors, our method halves the parameters of the original detection head and achieves high accuracy on the PASCAL VOC and MS COCO datasets. Moreover, the proposed method achieves superior performance compared with state-of-the-art methods based on similar framework.

Robust Localization of Retinal Lesions Via Weakly-Supervised Learning

Ruohan Zhao, Qin Li, Jane You

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Auto-TLDR; Weakly Learning of Lesions in Fundus Images Using Multi-level Feature Maps and Classification Score

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Retinal fundus images reveal the condition of retina, blood vessels and optic nerve. Retinal imaging is becoming widely adopted in clinical work because any subtle changes to the structures at the back of the eyes can affect the eyes and indicate the overall health. Machine learning, in particular deep learning by convolutional neural network (CNN), has been increasingly adopted for computer-aided detection (CAD) of retinal lesions. However, a significant barrier to the high performance of CNN based CAD approach is caused by the lack of sufficient labeled ground-truth image samples for training. Unlike the fully-supervised learning which relies on pixel-level annotation of pathology in fundus images, this paper presents a new approach to discriminate the location of various lesions based on image-level labels via weakly learning. More specifically, our proposed method leverages multi-level feature maps and classification score to cope with both bright and red lesions in fundus images. To enhance capability of learning less discriminative parts of objects (e.g. small blobs of microaneurysms opposed to bulk of exudates), the classifier is regularized by refining images with corresponding labels. The experimental results of the performance evaluation and benchmarking at both image-level and pixel-level on the public DIARETDB1 dataset demonstrate the feasibility and excellent potentials of our method in practice.

Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

Dayang Yu, Rong Zhang, Shan Qin

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Auto-TLDR; Cascade Saliency Attention Network for Object Detection in Remote Sensing Images

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Object detection in remote sensing images is a challenging task due to objects in the bird-view perspective appearing with arbitrary orientations. Though considerable progress has been made, there still exist challenges with the interference from complex backgrounds, dense arrangement, and large-scale variations. In this paper, we propose an oriented detector named Cascade Saliency Attention Network (CSAN), designed for comprehensively suppressing interference in remote sensing images. Specifically, we first combine context and pixel attention on feature maps to enhance saliency of objects for suppressing interference from backgrounds. Then, in cascade network, we apply instance segmentation on ROI to increase saliency of the central object, thus preventing object features from mutual interference in dense arrangement. Additionally, to alleviate large-scale variations, we devise a multi-scale merge module during FPN merging process to learn richer scale representations. Experimental results on DOTA and HRSC2016 datasets outperform other state-of-the-art object detection methods and verify the effectiveness of our method.

MANet: Multimodal Attention Network Based Point-View Fusion for 3D Shape Recognition

Yaxin Zhao, Jichao Jiao, Ning Li

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Auto-TLDR; Fusion Network for 3D Shape Recognition based on Multimodal Attention Mechanism

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3D shape recognition has attracted more and more attention as a task of 3D vision research. The proliferation of 3D data encourages various deep learning methods based on 3D data. Now there have been many deep learning models based on point-cloud data or multi-view data alone. However, in the era of big data, integrating data of two different modals to obtain a unified 3D shape descriptor is bound to improve the recognition accuracy. Therefore, this paper proposes a fusion network based on multimodal attention mechanism for 3D shape recognition. Considering the limitations of multi-view data, we introduce a soft attention scheme, which can use the global point-cloud features to filter the multi-view features, and then realize the effective fusion of the two features. More specifically, we obtain the enhanced multi-view features by mining the contribution of each multi-view image to the overall shape recognition, and then fuse the point-cloud features and the enhanced multi-view features to obtain a more discriminative 3D shape descriptor. We have performed relevant experiments on the ModelNet40 dataset, and experimental results verify the effectiveness of our method.

Early Wildfire Smoke Detection in Videos

Taanya Gupta, Hengyue Liu, Bir Bhanu

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Auto-TLDR; Semi-supervised Spatio-Temporal Video Object Segmentation for Automatic Detection of Smoke in Videos during Forest Fire

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Recent advances in unmanned aerial vehicles and camera technology have proven useful for the detection of smoke that emerges above the trees during a forest fire. Automatic detection of smoke in videos is of great interest to Fire department. To date, in most parts of the world, the fire is not detected in its early stage and generally it turns catastrophic. This paper introduces a novel technique that integrates spatial and temporal features in a deep learning framework using semi-supervised spatio-temporal video object segmentation and dense optical flow. However, detecting this smoke in the presence of haze and without the labeled data is difficult. Considering the visibility of haze in the sky, a dark channel pre-processing method is used that reduces the amount of haze in video frames and consequently improves the detection results. Online training is performed on a video at the time of testing that reduces the need for ground-truth data. Tests using the publicly available video datasets show that the proposed algorithms outperform previous work and they are robust across different wildfire-threatened locations.

SAT-Net: Self-Attention and Temporal Fusion for Facial Action Unit Detection

Zhihua Li, Zheng Zhang, Lijun Yin

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Auto-TLDR; Temporal Fusion and Self-Attention Network for Facial Action Unit Detection

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Research on facial action unit detection has shown remarkable performances by using deep spatial learning models in recent years, however, it is far from reaching its full capacity in learning due to the lack of use of temporal information of AUs across time. Since the AU occurrence in one frame is highly likely related to previous frames in a temporal sequence, exploring temporal correlation of AUs across frames becomes a key motivation of this work. In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a so-called SAT-Net) to address the AU detection problem. First of all, we input the deep features of a sequence into a convolutional LSTM network and fuse the previous temporal information into the feature map of the last frame, and continue to learn the AU occurrence. Second, considering the AU detection problem is a multi-label classification problem that individual label depends only on certain facial areas, we propose a new self-learned attention mask by focusing the detection of each AU on parts of facial areas through the learning of individual attention mask for each AU, thus increasing the AU independence without the loss of any spatial relations. Our extensive experiments show that the proposed framework achieves better results of AU detection over the state-of-the-arts on two benchmark databases (BP4D and DISFA).

MAGNet: Multi-Region Attention-Assisted Grounding of Natural Language Queries at Phrase Level

Amar Shrestha, Krittaphat Pugdeethosapol, Haowen Fang, Qinru Qiu

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Auto-TLDR; MAGNet: A Multi-Region Attention-Aware Grounding Network for Free-form Textual Queries

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Grounding free-form textual queries necessitates an understanding of these textual phrases and its relation to the visual cues to reliably reason about the described locations. Spatial attention networks are known to learn this relationship and focus its gaze on salient objects in the image. Thus, we propose to utilize spatial attention networks for image-level visual-textual fusion preserving local (word) and global (phrase) information to refine region proposals with an in-network Region Proposal Network (RPN) and detect single or multiple regions for a phrase query. We focus only on the phrase query - ground truth pair (referring expression) for a model independent of the constraints of the datasets i.e. additional attributes, context etc. For such referring expression dataset ReferIt game, our Multi- region Attention-assisted Grounding network (MAGNet) achieves over 12% improvement over the state-of-the-art. Without the con- text from image captions and attribute information in Flickr30k Entities, we still achieve competitive results compared to the state- of-the-art.

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

Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan

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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.

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

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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.

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

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

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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.

Skin Lesion Classification Using Weakly-Supervised Fine-Grained Method

Xi Xue, Sei-Ichiro Kamata, Daming Luo

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Auto-TLDR; Different Region proposal module for skin lesion classification

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In recent years, skin cancer has become one of the most common cancers. Among all types of skin cancers, melanoma is the most fatal one and many people die of this disease every year. Early detection can greatly reduce the death rate and save more lives. Skin lesions are one of the early symptoms of melanoma and other types of skin cancer. So accurately recognizing various skin lesions in early stage are of great significance. There have been lots of existing works based on convolutional neural networks (CNN) to solve skin lesion classification but seldom do them involve the similarity among different lesions. For example, we find that some lesions of melanoma and nevi look similar in appearance which is hard for neural network to distinguish categories of skin lesions. Inspired by fine-grained image classification, we propose a novel network to distinguish each category accurately. In our paper, we design an effective module, distinct region proposal module (DRPM), to extract the distinct regions from each image. Spatial attention and channel-wise attention are both utilized to enrich feature maps and guide the network to focus on the highlighted areas in a weakly-supervised way. In addition, two preprocessing steps are added to ensure the network to get better results. We demonstrate the potential of the proposed method on ISIC 2017 dataset. Experiments show that our approach is effective and efficient.

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

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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.

Self-Supervised Learning with Graph Neural Networks for Region of Interest Retrieval in Histopathology

Yigit Ozen, Selim Aksoy, Kemal Kosemehmetoglu, Sevgen Onder, Aysegul Uner

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Auto-TLDR; Self-supervised Contrastive Learning for Deep Representation Learning of Histopathology Images

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Deep learning has achieved successful performance in representation learning and content-based retrieval of histopathology images. The commonly used setting in deep learning-based approaches is supervised training of deep neural networks for classification, and using the trained model to extract representations that are used for computing and ranking the distances between images. However, there are two remaining major challenges. First, supervised training of deep neural networks requires large amount of manually labeled data which is often limited in the medical field. Transfer learning has been used to overcome this challenge, but its success remained limited. Second, the clinical practice in histopathology necessitates working with regions of interest (ROI) of multiple diagnostic classes with arbitrary shapes and sizes. The typical solution to this problem is to aggregate the representations of fixed-sized patches cropped from these regions to obtain region-level representations. However, naive methods cannot sufficiently exploit the rich contextual information in the complex tissue structures. To tackle these two challenges, we propose a generic method that utilizes graph neural networks (GNN), combined with a self-supervised training method using a contrastive loss. GNN enables representing arbitrarily-shaped ROIs as graphs and encoding contextual information. Self-supervised contrastive learning improves quality of learned representations without requiring labeled data. The experiments using a challenging breast histopathology data set show that the proposed method achieves better performance than the state-of-the-art.

Foreground-Focused Domain Adaption for Object Detection

Yuchen Yang, Nilanjan Ray

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Auto-TLDR; Unsupervised Domain Adaptation for Unsupervised Object Detection

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Object detectors suffer from accuracy loss caused by domain shift from a source to a target domain. Unsupervised domain adaptation (UDA) approaches mitigate this loss by training with unlabeled target domain images. A popular processing pipeline applies adversarial training that aligns the distributions of the features from the two domains. We advocate that aligning the full image level features is not ideal for UDA object detection due to the presence of varied background areas during inference. Thus, we propose a novel foreground-focused domain adaptation (FFDA) framework which mines the loss of the domain discriminators to concentrate on the backpropagation of foreground loss. We obtain mining masks by collecting target predictions and source labels to outline foreground regions, and apply the masks to image and instance level domain discriminators to allow backpropagation only on the mined regions. By reinforcing this foreground-focused adaptation throughout multiple layers in the detector model, we gain a significant accuracy boost on the target domain prediction. Compared to previous works, our method reaches the new state-of-the-art accuracy on adapting Cityscape to Foggy Cityscape dataset and demonstrates competitive accuracy on other datasets that include various scenarios for autonomous driving applications.

Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection

Ye He, Chao Zhu, Xu-Cheng Yin

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Auto-TLDR; A Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection

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State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion handling strategy of popular two-stage approaches is to build a two-branch architecture with the help of additional visible body annotations. Nonetheless, these methods still have some weaknesses. Either the two branches are trained independently with only score-level fusion, which cannot guarantee the detectors to learn robust enough pedestrian features. Or the attention mechanisms are exploited to only emphasize on the visible body features. However, the visible body features of heavily occluded pedestrians are concentrated on a relatively small area, which will easily cause missing detections. To address the above issues, we propose in this paper a novel Mutual-Supervised Feature Modulation (MSFM) network, to better handle occluded pedestrian detection. The key MSFM module in our network calculates the similarity loss of full body boxes and visible body boxes corresponding to the same pedestrian, so that the full-body detector could learn more complete and robust pedestrian features with the assist of contextual features from the occluding parts. To facilitate the MSFM module, we also propose a novel two-branch architecture, consisting of a standard full body detection branch and an extra visible body classification branch. These two branches are trained in a mutual-supervised way with full body annotations and visible body annotations, respectively. To verify the effectiveness of our proposed method, extensive experiments are conducted on two challenging pedestrian datasets: Caltech and CityPersons, and our approach achieves superior performances compared to other state-of-the-art methods on both datasets, especially in heavy occlusion cases.

Investigating and Exploiting Image Resolution for Transfer Learning-Based Skin Lesion Classification

Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Georg Dorffner, Isabella Ellinger

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Auto-TLDR; Fine-tuned Neural Networks for Skin Lesion Classification Using Dermoscopic Images

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Skin cancer is among the most common cancer types. Dermoscopic image analysis improves the diagnostic accuracy for detection of malignant melanoma and other pigmented skin lesions when compared to unaided visual inspection. Hence, computer-based methods to support medical experts in the diagnostic procedure are of great interest. Fine-tuning pre-trained convolutional neural networks (CNNs) has been shown to work well for skin lesion classification. Pre-trained CNNs are usually trained with natural images of a fixed image size which is typically significantly smaller than captured skin lesion images and consequently dermoscopic images are downsampled for fine-tuning. However, useful medical information may be lost during this transformation. In this paper, we explore the effect of input image size on skin lesion classification performance of fine-tuned CNNs. For this, we resize dermoscopic images to different resolutions, ranging from 64x64 to 768x768 pixels and investigate the resulting classification performance of three well-established CNNs, namely DenseNet-121, ResNet-18, and ResNet-50. Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification. We further propose a novel fusion approach based on a three-level ensemble strategy that exploits multiple fine-tuned networks trained with dermoscopic images at various sizes. When applied on the ISIC 2017 skin lesion classification challenge, our fusion approach yields an area under the receiver operating characteristic curve of 89.2% and 96.6% for melanoma classification and seborrheic keratosis classification, respectively, outperforming state-of-the-art algorithms.

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

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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.

Bidirectional Matrix Feature Pyramid Network for Object Detection

Wei Xu, Yi Gan, Jianbo Su

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Auto-TLDR; BMFPN: Bidirectional Matrix Feature Pyramid Network for Object Detection

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Feature pyramids are widely used to improve scale invariance for object detection. Most methods just map the objects to feature maps with relevant square receptive fields, but rarely pay attention to the aspect ratio variation, which is also an important property of object instances. It will lead to a poor match between rectangular objects and assigned features with square receptive fields, thus preventing from accurate recognition and location. Besides, the information propagation among feature layers is sparse, namely, each feature in the pyramid may mainly or only contain single-level information, which is not representative enough for classification and localization sub-tasks. In this paper, Bidirectional Matrix Feature Pyramid Network (BMFPN) is proposed to address these issues. It consists of three modules: Diagonal Layer Generation Module (DLGM), Top-down Module (TDM) and Bottom-up Module (BUM). First, multi-level features extracted by backbone are fed into DLGM to produce the base features. Then these base features are utilized to construct the final feature pyramid through TDM and BUM in series. The receptive fields of the designed feature layers in BMFPN have various scales and aspect ratios. Objects can be correctly assigned to appropriate and representative feature maps with relevant receptive fields depending on its scale and aspect ratio properties. Moreover, TDM and BUM form bidirectional and reticular information flow, which effectively fuses multi level information in top-down and bottom-up manner respectively. To evaluate the effectiveness of our proposed architecture, an end-toend anchor-free detector is designed and trained by integrating BMFPN into FCOS. And the center ness branch in FCOS is modified with our Gaussian center-ness branch (GCB), which brings another slight improvement. Without bells and whistles, our method gains +3.3%, +2.4% and +2.6% AP on MS COCO dataset from baselines with ResNet-50, ResNet-101 and ResNeXt-101 backbones, respectively.

PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation

Yue Liu, Zhichao Lian

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Auto-TLDR; Pyramid Pooling Module with SE1Cblock and D2SUpsample Network (PSDNet)

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Abstract—In this paper, we present our Pyramid Pooling Module （PPM） with SE1Cblock and D2SUpsample Network (PSDNet), a novel architecture for accurate semantic segmentation. Started from the known work called Pyramid Scene Parsing Network (PSPNet), PSDNet takes advantage of pyramid pooling structure with channel attention module and feature transform module in Pyramid Pooling Module (PPM). The enhanced PPM with these two components can strengthen context information flowing in the network instead of damaging it. The channel attention module we mentioned is an improved “Squeeze and Excitation with 1D Convolution” (SE1C) block which can explicitly model interrelationship between channels with fewer number of parameters. We propose a feature transform module named “Depth to Space Upsampling” (D2SUpsample) in the PPM which keeps integrity of features by transforming features while interpolating features, at the same time reducing parameters. In addition, we introduce a joint strategy in SE1Cblock which combines two variants of global pooling without increasing parameters. Compared with PSPNet, our work achieves higher accuracy on public datasets with 73.97% mIoU and 82.89% mAcc accuracy on Cityscapes Dataset based on ResNet50 backbone.

Point In: Counting Trees with Weakly Supervised Segmentation Network

Pinmo Tong, Shuhui Bu, Pengcheng Han

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Auto-TLDR; Weakly Tree counting using Deep Segmentation Network with Localization and Mask Prediction

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For tree counting tasks, since traditional image processing methods require expensive feature engineering and are not end-to-end frameworks, this will cause additional noise and cannot be optimized overall, so this method has not been widely used in recent trends of tree counting application. Recently, many deep learning based approaches are designed for this task because of the powerful feature extracting ability. The representative way is bounding box based supervised method, but time-consuming annotations are indispensable for them. Moreover, these methods are difficult to overcome the occlusion or overlap. To solve this problem, we propose a weakly tree counting network (WTCNet) based on deep segmentation network with only point supervision. It can simultaneously complete tree counting with localization and output mask of each tree at the same time. We first adopt a novel feature extractor network (FENet) to get features of input images, and then an effective strategy is introduced to deal with different mask predictions. In the end, we propose a basic localization guidance accompany with rectification guidance to train the network. We create two different datasets and select an existing challenging plant dataset to evaluate our method on three different tasks. Experimental results show the good performance improvement of our method compared with other existing methods. Further study shows that our method has great potential to reduce human labor and provide effective ground-truth masks and the results show the superiority of our method over the advanced methods.

Hybrid Cascade Point Search Network for High Precision Bar Chart Component Detection

Junyu Luo, Jinpeng Wang, Chin-Yew Lin

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Auto-TLDR; Object Detection of Chart Components in Chart Images Using Point-based and Region-Based Object Detection Framework

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Charts are commonly used for data visualization. One common form of chart distribution is in its image form. To enable machine comprehension of chart images, precise detection of chart components in chart images is a critical step. Existing image object detection methods do not perform well in chart component detection which requires high boundary detection precision. And traditional rule-based approaches lack enough generalization ability. In order to address this problem, we design a novel two-stage object detection framework that combines point-based and region-based ideas, by simulating the process that human creating bounding boxes for objects. The experiment on our labeled ChartDet dataset shows our method greatly improves the performance of chart object detection. We further extend our method to a general object detection task and get comparable performance.

MagnifierNet: Learning Efficient Small-Scale Pedestrian Detector towards Multiple Dense Regions

Qi Cheng, Mingqin Chen, Yingjie Wu, Fei Chen, Shiping Lin

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Auto-TLDR; MagnifierNet: A Simple but Effective Small-Scale Pedestrian Detection Towards Multiple Dense Regions

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Despite the success of pedestrian detection, there is still a significant gap in the performance of the detection of pedestrians at different scales. Detecting small-scale pedestrians is extremely challenging due to the low resolution of their convolution features which is essential for downstream classifiers. To address this issue, we observed pedestrian datasets and found that pedestrians often gather together in crowded public places. Then we propose MagnifierNet, a simple but effective small-scale pedestrian detector towards multiple dense regions. MagnifierNet uses our proposed sweep-line based grouping algorithm to find dense regions based on the number of pedestrians in the grouped region. And we adopt a new definition of small-scale pedestrians through grid search and KL-divergence. Besides, our grouping method can also be used as a new strategy for pedestrian data augmentation. The ablation study demonstrates that MagnifierNet improves the representation of small-scale pedestrians. We validate the effectiveness of MagnifierNet on CityPersons and KITTI datasets. Experimental results show that MagnifierNet achieves the best small-scale pedestrian detection performance on CityPersons benchmark without any external data, and also achieves competitive performance for detecting small-scale pedestrians on KITTI dataset without bells and whistles.

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

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

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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.

CASNet: Common Attribute Support Network for Image Instance and Panoptic Segmentation

Xiaolong Liu, Yuqing Hou, Anbang Yao, Yurong Chen, Keqiang Li

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Auto-TLDR; Common Attribute Support Network for instance segmentation and panoptic segmentation

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Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentation, instance segmentation can provide more analytical results at pixel level. Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes. CASNet is designed in the manner of fully convolutional and can implement training and inference from end to end. And CASNet manages predicting the instance without overlaps and holes, which problem exists in most of current instance segmentation algorithms. Furthermore, it can be easily extended to panoptic segmentation through minor modifications with little computation overhead. CASNet builds a bridge between semantic and instance segmentation from finding pixel class ID to obtaining class and instance ID by operations on common attribute. Through experiment for instance and panoptic segmentation, CASNet gets mAP 32.8\% and PQ 59.0\% on Cityscapes validation dataset by joint training, and mAP 36.3\% and PQ 66.1\% by separated training mode. For panoptic segmentation, CASNet gets state-of-the-art performance on the Cityscapes validation dataset.