M3d-CAM

Abstract

Deep learning models achieve state-of-the-art results in a wide array of medical imaging problems. Yet the lack of interpretability of deep neural networks is a primary concern for medical practitioners and poses a considerable barrier before the deployment of such models in clinical practice. Several techniques have been developed for visualizing the decision process of DNNs. However, few implementations are openly available for the popular PyTorch library, and existing implementations are often limited to two-dimensional data and classification models. We present M3d-CAM, an easy easy to use library for generating attention maps of CNN-based PyTorch models for both 2D and 3D data, and applicable to both classification and segmentation models. The attention maps can be generated with multiple methods: Guided Backpropagation, Grad-CAM, Guided Grad-CAM and Grad-CAM++. The maps visualize the regions in the input data that most heavily influence the model prediction at a certain layer. Only a single line of code is sufficient for generating attention maps for a model, making M3d-CAM a plug-and-play solution that requires minimal previous knowledge.