A Dynamic Group Equivariant Convolutional Networks for Medical Image Analysis

Group equivariant Convolutional Neural Networks (G-CNNs) has led to big empirical success in the medical domain, one fundamental assumption is that equivariance provides a powerful inductive bias for medical images. By leveraging concepts from group representation theory, we can generalize vanilla Convolutional Neural Networks (CNNs) to G-CNN. Currently, although embedding an arbitrary equivariance to CNNs can learn powerful disentangled representations in a higher dimensional domain, they lack explicit means to learn meaningful relationships among the equivariant convolutional kernels. In this paper, we propose a generalization of the dynamic convolutional method, named as dynamic group equivariant convolution, to strengthen the relationships and increase model capability by aggregating multiple group convolutional kernels via attention. Meanwhile, we generalize attention to an equivariant one to preserve equivariant of dynamic group convolution. In our approach, this leads to a flexible framework that enables a dynamic convolutional in G-CNNs by means of a dynamic routing layer expansions. We demonstrate that breast tumor classification is substantial improvements when compared to a recent baseline architecture.