MedEBench：诊断文本引导的医学图像编辑中的可靠性
MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing
AI总结：
针对文本引导医学图像编辑缺乏标准化评估的问题，提出包含1182组临床图像-提示对的MedEBench基准，构建三维评估框架，对比7个SOTA模型并提出基于注意力对齐的错误分析方法，为可靠编辑工具研发奠基。
摘要：
文本引导的图像编辑在自然图像领域已取得显著进展，但在医学成像中的应用仍然有限，且缺乏标准化评估框架。这类编辑可通过实现个性化手术规划、强化医学教育以及改善医患沟通，为临床实践带来变革。为填补这一空白，我们推出了MedEBench——一个专为诊断文本引导医学图像编辑可靠性而设计的稳健基准。MedEBench包含1182组经临床筛选的图像-提示词对，覆盖70项不同编辑任务和13个解剖区域。它在三个关键领域做出贡献：（1）一套基于临床的评估框架，用于衡量编辑准确性、上下文保存度和视觉质量，并配有预期编辑的详细说明及对应的感兴趣区域（ROI）掩码；（2）对7个最先进模型的全面比较，揭示了一致的失败模式；（3）一种利用注意力对齐的诊断性错误分析技术，通过模型注意力图与ROI掩码之间的交并比（IoU）来识别定位错误问题——即模型错误地聚焦于错误的解剖区域。MedEBench为开发更可靠、临床更有效的文本引导医学图像编辑工具奠定了基础。
英文摘要：
Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited and lacks standardized evaluation frameworks. Such editing could revolutionize clinical practices by enabling personalized surgical planning, enhancing medical education, and improving patient communication. To bridge this gap, we introduce MedEBench1, a robust benchmark designed to diagnose reliability in text-guided medical image editing. MedEBench consists of 1,182 clinically curated image-prompt pairs covering 70 distinct editing tasks and 13 anatomical regions. It contributes in three key areas: (1) a clinically grounded evaluation framework that measures Editing Accuracy, Context Preservation, and Visual Quality, complemented by detailed descriptions of intended edits and corresponding Region-of-Interest (ROI) masks; (2) a comprehensive comparison of seven state-of-theart models, revealing consistent patterns of failure; and (3) a diagnostic error analysis technique that leverages attention alignment, using Intersection-over-Union (IoU) between model attention maps and ROI masks to identify mislocalization issues, where models erroneously focus on incorrect anatomical regions. MedEBench sets the stage for developing more reliable and clinically effective text-guided medical image editing tools.
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。
