Multimodal Extension of the ML-CSC Framework for Medical Image Segmentation

Abstract

In recent years, Convolutional Neural Networks (CNNs) have led to huge successes across various computer vision applications. However, the lack of interpretability poses a severe barrier for their wider adoption in healthcare. Recently introduced Multilayer Convolutional Sparse Coding (ML-CSC) data model provides a model-based explanation of CNNs. This article aims to extend the ML-CSC framework towards multimodal data processing, which to our knowledge has not been addressed so far. In particular, we focus on interpretable medical image segmentation architecture design for multimodal data. We derive a novel sparse coding algorithm and propose three different CNN architectures with increasing performance, without introducing any additional learnable parameters. Based on the sparse coding theory, our multimodal extension enables the systematic design of interpretable CNN segmentation architectures. Experimental analysis demonstrates that the achieved segmentation results are consistent with the obtained theoretical expectations.