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.

通过 ProtoVerse 方法，我们成功应用于 VerSe'19 数据集，解决了骨折分级和深度学习模型解释性不足的问题，实现了对医学诊断的可解释性和临床适用性的提升。