Visualizing Multimodal Deep Learning for Lesion Prediction
Christina Gillmann, Lucas Peter, Carlo Schmidt, Dorothee Saur, Gerik Scheuermann
Abstract
A U-Net is a type of convolutional neural network that has been shown to output impressive results in medical imaging segmentation tasks. Still, neural networks in general form a black box that is hard to interpret, especially by noncomputer scientists. This work provides a visual system that allows users to examine U-Nets that were trained to predict brain lesions caused by stroke using multimodal imaging. We provide several visualization views that allow users to load trained U-Nets, run them on different patient data, and examine the results while visually following the computation of the U-Net. With these visualizations, we can provide useful information for our medical collaborators showing how the training database can be improved and which features are best learned by the neural network.