Patch Level Segmentation and Visualization of Capsule Network Inference for Breast Metastases Detection

Capsule Networks are becoming popular for developing AI-guided medical diagnostic tools. The objective of this paper is to carve out a strategy to solve dual problems of classification and segmentation of metastatic tissue regions in one single pipeline. To accomplish this, an attempt has been made in this paper to utilize capsule networks with variational Baye’s routing to classify normal and metastatic tissue regions from breast cancer whole slide images. Thereafter, a high-level segmentation of the metastatic tissue region has been carried out using the classified patches. The results obtained with a set of 75,000 patches show that patch-level segmentation is an efficient method to delineate metastatic regions. In the prospect of end-users, visualization of results plays a significant role in selecting the appropriate method for their applications. Capsule networks mimic the way the human brain works. For long, it has been the demand from clinicians that the algorithms used for the automatic classification of cancer pathology should be interpretable. Thus, in clinical practice, such a method will be more acceptable. The efficient region segmentation would aid clinicians in readily demarcating the area of interest and the area of most relevance.