Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification
This addresses the problem of interpretability in high-risk medical applications for clinicians, though it is incremental as it builds on existing Vision Transformer methods.
The paper tackled the need for explainability in medical image classification by introducing HierViT, a Vision Transformer that integrates human-defined features and prototypes for interpretability, achieving superior accuracy on lung nodule assessment and comparable accuracy on skin lesion classification.
Explainability is a highly demanded requirement for applications in high-risk areas such as medicine. Vision Transformers have mainly been limited to attention extraction to provide insight into the model's reasoning. Our approach combines the high performance of Vision Transformers with the introduction of new explainability capabilities. We present HierViT, a Vision Transformer that is inherently interpretable and adapts its reasoning to that of humans. A hierarchical structure is used to process domain-specific features for prediction. It is interpretable by design, as it derives the target output with human-defined features that are visualized by exemplary images (prototypes). By incorporating domain knowledge about these decisive features, the reasoning is semantically similar to human reasoning and therefore intuitive. Moreover, attention heatmaps visualize the crucial regions for identifying each feature, thereby providing HierViT with a versatile tool for validating predictions. Evaluated on two medical benchmark datasets, LIDC-IDRI for lung nodule assessment and derm7pt for skin lesion classification, HierViT achieves superior and comparable prediction accuracy, respectively, while offering explanations that align with human reasoning.