M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning
This provides an easy-to-use tool for improving model interpretability in medical imaging, but it is incremental as it adapts existing methods to 3D data and PyTorch.
The authors tackled the problem of interpreting CNN-based PyTorch models in medical deep learning by developing M3d-CAM, a library that generates 2D and 3D attention maps with methods like Grad-CAM and Guided Backpropagation, requiring only a single line of code in most cases.
M3d-CAM is an easy to use library for generating attention maps of CNN-based PyTorch models improving the interpretability of model predictions for humans. The attention maps can be generated with multiple methods like Guided Backpropagation, Grad-CAM, Guided Grad-CAM and Grad-CAM++. These attention maps visualize the regions in the input data that influenced the model prediction the most at a certain layer. Furthermore, M3d-CAM supports 2D and 3D data for the task of classification as well as for segmentation. A key feature is also that in most cases only a single line of code is required for generating attention maps for a model making M3d-CAM basically plug and play.