Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations

This repository contains Python code and bash files to train and explain prototypical parts learned by the Prototypical Part Network (ProtoPNet).

This work implements ProtoPNet and Prototipical descriptors to explain an image classification, quantify the influence of color hue, shape, texture, contrast, saturation and brightness in each prototype locally and globally and evaluate performance of the trained model under such perturbations.

Corresponding paper on ArXiv: Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations