利用人类可解释原型增强椎体骨折分级可解释性
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
AI总结：
本文提出按设计可解释方法ProtoVerse，通过多样性促进损失学习椎体骨折原型，在VerSe'19上优于现有原型方法并获放射科专家验证。
摘要：
椎体骨折分级旨在对椎体骨折的严重程度进行分类，这是医学影像中的一项挑战性任务，近期已引起深度学习（DL）模型的关注。尽管在深度学习辅助医学诊断等关键用例中需要透明性和可信度，但只有少数工作尝试使此类模型具备人类可解释性。此外，这些模型要么依赖事后解释方法，要么依赖额外标注。在本工作中，我们提出了一种新颖的按设计可解释方法 ProtoVerse，用于寻找椎体骨折的相关子部分（原型），从而以人类可理解的方式可靠地解释模型决策。具体而言，我们引入了一种新颖的多样性促进损失，以缓解具有复杂语义的小型数据集中的原型重复问题。我们在 VerSe'19 数据集上进行了实验，并优于现有的基于原型的方法。此外，与事后解释方法相比，我们的模型提供了更优的可解释性。重要的是，放射科专家验证了我们结果的可视化可解释性，显示出临床适用性。
英文摘要：
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.
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