Visualizing Uncertainty and Saliency Maps of Deep Convolutional Neural Networks for Medical Imaging Applications
This addresses the need for interpretability and safety in medical AI applications, but appears incremental as it combines existing visualization techniques.
The authors tackled the problem of improving trust in deep learning models for medical imaging by developing a pipeline that visualizes both model uncertainty and saliency maps, though no concrete results or numbers are provided.
Deep learning models are now used in many different industries, while in certain domains safety is not a critical issue in the medical field it is a huge concern. Not only, we want the models to generalize well but we also want to know the models confidence respect to its decision and which features matter the most. Our team aims to develop a full pipeline in which not only displays the uncertainty of the models decision but also, the saliency map to show which sets of pixels of the input image contribute most to the predictions.