可解释的医学影像诊断与自注意力Transformer：医疗保健可解释人工智能综述
Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care
- University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本综述聚焦可解释人工智能在医学影像诊断中的应用，总结视觉Transformer的最新进展及其解释方法，以提升医疗决策的透明度。
AI中文摘要：
近年来，人工智能（AI）的进步促进了其在初级医疗服务中的广泛应用，解决了医疗保健领域的供需失衡问题。视觉Transformer（ViT）凭借自注意力模块已成为最先进的计算机视觉模型。然而，与传统机器学习方法相比，深度学习模型复杂且常被视为“黑箱”，这可能导致对其运作方式的不确定性。可解释人工智能（XAI）是指解释和解读机器学习模型内部运作及其决策过程的方法，这在医疗领域尤为重要，以指导医疗决策过程。本综述总结了ViT的最新进展以及理解ViT决策过程的解释性方法，从而在医学诊断应用中实现透明度。
英文摘要：
Recent advancements in artificial intelligence (AI) have facilitated its widespread adoption in primary medical services, addressing the demand-supply imbalance in healthcare. Vision Transformers (ViT) have emerged as state-of-the-art computer vision models, benefiting from self-attention modules. However, compared to traditional machine-learning approaches, deep-learning models are complex and are often treated as a "black box" that can cause uncertainty regarding how they operate. Explainable Artificial Intelligence (XAI) refers to methods that explain and interpret machine learning models' inner workings and how they come to decisions, which is especially important in the medical domain to guide the healthcare decision-making process. This review summarises recent ViT advancements and interpretative approaches to understanding the decision-making process of ViT, enabling transparency in medical diagnosis applications.
