Feature Gradient Flow for Interpreting Deep Neural Networks in Head and Neck Cancer Prediction
This work addresses the need for interpretable AI in medical diagnosis, specifically for oncologists using CT scans, but it is incremental as it builds on existing gradient-based interpretation methods.
The paper tackled the problem of interpreting deep learning models in medical imaging by introducing feature gradient flow, a technique that measures how well interpretable features align with model gradients, and tested it on a convolutional neural network for predicting distant metastasis in head and neck cancer from CT scans, achieving improved interpretability.
This paper introduces feature gradient flow, a new technique for interpreting deep learning models in terms of features that are understandable to humans. The gradient flow of a model locally defines nonlinear coordinates in the input data space representing the information the model is using to make its decisions. Our idea is to measure the agreement of interpretable features with the gradient flow of a model. To then evaluate the importance of a particular feature to the model, we compare that feature's gradient flow measure versus that of a baseline noise feature. We then develop a technique for training neural networks to be more interpretable by adding a regularization term to the loss function that encourages the model gradients to align with those of chosen interpretable features. We test our method in a convolutional neural network prediction of distant metastasis of head and neck cancer from a computed tomography dataset from the Cancer Imaging Archive.