Despite many recent advances in machine learning and time-series classification, accurate prediction of seizures remains elusive. In this work, we develop a convolutional network module that uses Electroencephalogram (EEG) scalograms to distinguish between the pre-seizure and normal brain activities. Since the EEG scalogram takes rectangular image format with many more temporal bins than spectral bins, the presented module uses "semi-dilated convolutions" to also create a proportional non-square receptive field. The proposed semi-dilated convolutions support exponential expansion of the receptive field over the long dimension (image width, i.e. time) while maintaining high resolution over the short dimension (image height, i.e., frequency). The proposed architecture comprises a set of co-operative semi-dilated convolutional blocks, each block has a stack of parallel semi-dilated convolutional modules with different dilation rates. Results show that our proposed seizure prediction solution outperforms the state-of-the-art methods, achieving a seizure prediction sensitivity of 88.45% and 89.52% for the American Epilepsy Society and Melbourne University EEG datasets, respectively.