Pseudo-class Part Prototype Networks For Interpretable Breast Cancer Classification

This article discusses the importance of interpretability in machine learning, particularly in high-stakes applications such as healthcare. The authors propose a new method that leverages clustering to improve accuracy and interpretability in breast cancer classification using histopathology images.