E pluribus unum interpretable convolutional neural networks

Datasets

EPU-CNN was trained and evaluated on nine different datasets. Initially, a dataset specifically created for the evaluation of the interpretability capabilities of EPU-CNN was considered. The purpose of using this dataset was to demonstrate the capabilities of EPU-CNN with clear, simple, and perceptually meaningful examples. Considering biomedicine as a critical application area for explainable and interpretable artificial intelligence (AI), four well-known biomedical benchmark datasets, consisting of endoscopic and dermoscopic images, was used for further evaluation. Furthermore,…