ProtoPatchNet: An Interpretable Patch-Based Prototypical Network

Abstract

The growing prevalence of AI solutions in high-stakes decision domains, like healthcare, necessitates forgoing traditional black-box models in favor of inherently interpretable models. Self-explaining prototype-based models are an exciting research direction, and transparency is an essential desideratum for such models. However, current methods either rely on an ambiguous step of replacing learned prototypes with the nearest training data points (part prototypebased models) or are limited by relying on the joint optimization of a decoder along with the classifier to enable visualization of the actual learned prototypes (global prototype-based models). Addressing existing gaps, we propose ProtoPatchNet, an interpretable model that elucidates the model's reasoning process, traceable in terms of visualization of the 'parts' of the learned prototypes that contribute to the final prediction. We evaluate the approach for glaucoma detection on the benchmark RIM-ONE DL dataset. Results show that ProtoPatchNet achieves performance comparable to uninterpretable black-box models and global prototype-based models while surpassing existing part prototype-based baselines.