Patchnet: Interpretable Neural Networks for Image Classification

The ability to visually understand and interpret learned features from complex predictive models is crucial for their acceptance in sensitive areas such as health care. To move closer to this goal of truly interpretable complex models, we present PatchNet, a network that restricts global context for image classification tasks in order to easily provide visual representations of learned texture features on a predetermined local scale. We demonstrate how PatchNet provides visual heatmap representations of the learned features, and we mathematically analyze the behavior of the network during convergence. We also present a version of PatchNet that is particularly well suited for lowering false positive rates in image classification tasks. We apply PatchNet to the classification of textures from the Describable Textures Dataset and to the ISBI-ISIC 2016 melanoma classification challenge. Via these examples, we show that in addition to providing interpretable features, some of the filters used in PatchNet also directly perform image segmentation.