X-SiT让脑部表面图像诊断更透明，能解释为何判断为阿尔茨海默病
X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis
- 用脑皮层表面图构建可解释的视觉变压器，通过原型匹配分析特征
- 在阿尔茨海默和额颞叶痴呆检测上达到顶尖准确率
- 生成与医学知识一致的可视原型，适合临床医生理解模型决策
可解释模型对支持临床决策至关重要，推动其在医学影像中的发展与应用。然而，3D体数据本身难以可视化和解释复杂的脑结构，如大脑皮层。相比之下，皮层表面渲染提供了更直观易懂的3D脑解剖表示，有利于可视化与交互探索。基于这一优势及表面数据在神经疾病研究中的广泛应用，我们提出可解释的表面视觉变压器（X-SiT）。这是首个具备内在可解释性的神经网络，其预测基于人类可理解的皮层特征。X-SiT引入一种原型化表面块解码器，通过空间对应的皮层原型进行分类，结合案例推理机制。实验表明，该模型在阿尔茨海默病与额颞叶痴呆检测中达到当前最优性能，同时生成与已知疾病模式一致的可解释原型，并揭示分类错误原因。
原文摘要 · Abstract (English)
Interpretable models are crucial for supporting clinical decision-making, driving advances in their development and application for medical images. However, the nature of 3D volumetric data makes it inherently challenging to visualize and interpret intricate and complex structures like the cerebral cortex. Cortical surface renderings, on the other hand, provide a more accessible and understandable 3D representation of brain anatomy, facilitating visualization and interactive exploration. Motivated by this advantage and the widespread use of surface data for studying neurological disorders, we present the eXplainable Surface Vision Transformer (X-SiT). This is the first inherently interpretable neural network that offers human-understandable predictions based on interpretable cortical features. As part of X-SiT, we introduce a prototypical surface patch decoder for classifying surface patch embeddings, incorporating case-based reasoning with spatially corresponding cortical prototypes. The results demonstrate state-of-the-art performance in detecting Alzheimer's disease and frontotemporal dementia while additionally providing informative prototypes that align with known disease patterns and reveal classification errors.
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