Work

Ten simple rules for engaging with artificial intelligence in biomedicine

Year: 2021

Type: article

Abstract: We propose a technique for producing 'visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent and explainable.Our approach -Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say 'dog' in a classification network or a sequence of words in captioning network) flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept.Unlike previous approaches, Grad-CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fullyconnected layers (e.g.VGG), (2) CNNs used for structured outputs (e.g.captioning), (3) CNNs used in tasks with multimodal inputs (e.g.visual question answering) or reinforcement learning, all without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative vi-Ramprasaath

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1008531