Stay Focused - Enhancing Model Interpretability Through Guided Feature Training

Alexander Jenke, Sebastian Bodenstedt, Martin Wagner, Johanna M. Brandenburg, Antonia Stern, Lars Mündermann, Marius Distler, Jürgen Weitz, Beat P. Müller-Stich, Stefanie Speidel

Abstract: In computer-assisted surgery, artificial intelligence (AI) methods need to be interpretable, as a clinician has to understand a model’s decision. To improve the visual interpretability of convolutional neural network, we propose to indirectly guide the feature development process of the model with augmented training data in which unimportant regions in an image have been blurred. On a public dataset, we show that our proposed training workflow results in better visual interpretability of the model and improves the overall model performance. To numerically evaluate heat maps, produced by explainable AI methods, we propose a new metric evaluating the focus with regards to a mask of the region of interest. Further, we are able to show that the resulting model is more robust against changes in the background by focusing the features onto the important areas of the scene and therefore improve model generalization.