基于原型的分层视觉Transformer用于可解释医学图像分类
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification
AI总结：
针对医疗图像分类的可解释性需求，提出HierViT分层视觉Transformer，通过原型可视化人类定义特征、结合领域知识并生成注意力热力图，在两个医疗数据集上实现高精度且解释与人类推理一致。
摘要：
可解释性是医疗等高风险领域应用的高需求要求。视觉Transformer主要局限于通过注意力提取来洞察模型推理过程。我们的方法将视觉Transformer的高性能与新的可解释能力相结合。我们提出HierViT，一种本质上可解释且能将其推理过程适配人类推理的视觉Transformer。该模型采用分层结构处理特定领域特征以进行预测，其设计具备可解释性：通过人类定义的特征（由示例图像即原型可视化）推导目标输出。通过整合这些决定性特征的领域知识，其推理在语义上与人类推理相似，因此具有直观性。此外，注意力热力图可视化识别每个特征的关键区域，为HierViT提供验证预测的多功能工具。在两个医疗基准数据集（用于肺结节评估的LIDC-IDRI和用于皮肤病变分类的derm7pt）上评估，HierViT分别实现了更优和可比的预测精度，同时提供与人类推理一致的解释。
英文摘要：
Explainability is a highly demanded requirement for applications in high-risk areas such as medicine. Vision Transformers have mainly been limited to attention extraction to provide insight into the model's reasoning. Our approach combines the high performance of Vision Transformers with the introduction of new explainability capabilities. We present HierViT, a Vision Transformer that is inherently interpretable and adapts its reasoning to that of humans. A hierarchical structure is used to process domain-specific features for prediction. It is interpretable by design, as it derives the target output with human-defined features that are visualized by exemplary images (prototypes). By incorporating domain knowledge about these decisive features, the reasoning is semantically similar to human reasoning and therefore intuitive. Moreover, attention heatmaps visualize the crucial regions for identifying each feature, thereby providing HierViT with a versatile tool for validating predictions. Evaluated on two medical benchmark datasets, LIDC-IDRI for lung nodule assessment and derm7pt for skin lesion classification, HierViT achieves superior and comparable prediction accuracy, respectively, while offering explanations that align with human reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
