Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
This work addresses the need for transparency in deep learning-assisted medical diagnosis for radiologists, though it is incremental as it builds on prototype-based methods.
The paper tackled the challenge of making vertebral fracture grading models interpretable by proposing ProtoVerse, a novel interpretable-by-design method that identifies human-understandable prototypes to explain decisions, outperforming existing prototype-based methods and gaining validation from expert radiologists.
Vertebral fracture grading classifies the severity of vertebral fractures, which is a challenging task in medical imaging and has recently attracted Deep Learning (DL) models. Only a few works attempted to make such models human-interpretable despite the need for transparency and trustworthiness in critical use cases like DL-assisted medical diagnosis. Moreover, such models either rely on post-hoc methods or additional annotations. In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way. Specifically, we introduce a novel diversity-promoting loss to mitigate prototype repetitions in small datasets with intricate semantics. We have experimented with the VerSe'19 dataset and outperformed the existing prototype-based method. Further, our model provides superior interpretability against the post-hoc method. Importantly, expert radiologists validated the visual interpretability of our results, showing clinical applicability.