当编辑改变患者时：在反事实视网膜图像中测量身份保留情况

中文摘要 AI 辅助

反事实医学图像生成旨在修改现有图像以反映成像对象某些特征被改变的假设场景，同时保持其身份固定。现有大多数研究复用已建立的图像编辑方法，这些方法未直接监督身份保留，而是假设通过将生成锚定到源图像来隐式保留身份。该假设很少被验证，在生物特征线索微妙的领域（如视网膜光学相干断层扫描（OCT））可能不成立。本研究使用裁判分类器、嵌入对齐分数和盲读研究，明确测量三组文本条件编辑方法（源锚定、结构化提示、配对训练）的身份保留情况。我们发现所有方法生成的OCT图像质量高、编辑成功率相当，但身份保留差异显著：源锚定编辑常改变所描绘对象，配对训练的身份保留效果最佳。我们认为，未来医学反事实生成研究必须在图像真实感和编辑成功率之外，明确测量并报告身份保留情况。

英文摘要

Counterfactual medical image generation aims to modify an existing image to reflect a hypothetical scenario in which certain characteristics of the imaged subject are altered, while keeping their identity fixed. Most existing works repurpose established image editing methods, which do not directly supervise identity preservation. Instead, they assume that identity is implicitly preserved by anchoring generation to the source image. This assumption is rarely tested and may fail in domains where biometric cues are subtle, such as retinal optical coherence tomography (OCT). In this work, we explicitly measure identity preservation for three groups of text-conditioned editing methods - source-anchored, structured-prompt, and paired-training - using referee classifiers, embedding alignment scores, and a blind reader study. We find that all methods produce high-quality OCT images with comparable editing success, yet their identity preservation differs markedly. Source-anchored editing frequently alters the depicted subject, while paired-training preserves it best. We argue that future work on medical counterfactual generation must explicitly measure and report identity preservation alongside image realism and editing success.

发表机构

• Technical University of Munich（慕尼黑工业大学）
• TUM University Hospital（慕尼黑工业大学医院）
• Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）
• Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
• Sapienza University of Rome（罗马第一大学）
• Faculty of Medicine, University of Southampton（南安普顿大学医学院）
• Moorfields Eye Hospital NHS Foundation Trust（摩尔菲尔兹眼科医院NHS信托基金）
• Imperial College London（伦敦帝国学院）

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