标题： X-SiT：用于痴呆诊断的固有可解释表面视觉变压器 显示英文标题

标题： X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis

摘要： 可解释的模型对于支持临床决策至关重要，推动了其在医学图像中的开发和应用进展。 然而，三维体积数据的性质使得可视化和解释像大脑皮层这样的复杂结构本质上具有挑战性。 另一方面，皮层表面渲染提供了更易于访问和理解的脑解剖三维表示，有助于可视化和交互式探索。 受这一优势以及表面数据在研究神经疾病中的广泛应用的启发，我们提出了可解释的表面视觉变压器（X-SiT）。 这是第一个本质上可解释的神经网络，它基于可解释的皮层特征提供人类可理解的预测。 作为X-SiT的一部分，我们引入了一个原型表面块解码器，用于对表面块嵌入进行分类，结合基于案例的推理与空间对应的皮层原型。 结果表明，在检测阿尔茨海默病和额颞叶痴呆方面达到了最先进的性能，同时提供了与已知疾病模式一致且揭示分类错误的信息性原型。 显示英文摘要

摘要： 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.