检验医学影像编辑中身份保持效果，发现不同方法差异显著。
When the Edit Changes the Patient: Measuring Identity Preservation in Counterfactual Retinal Images
- 用裁判分类器等方法直接评估编辑后身份一致性
- 配对训练法身份保留最好，源锚定法常改变患者特征
- 提醒未来研究需同时报告真实性、编辑效果和身份保持
反事实医学图像生成旨在修改现有图像以反映假设情景下受试者特定特征的变化，同时保持其身份不变。现有方法多复用通用图像编辑技术，未直接监督身份保持，仅假设通过锚定源图像可隐式保留身份。这一假设在生物特征细微的领域（如视网膜OCT）很少被验证，可能失效。本文通过裁判分类器、嵌入对齐分数和盲读研究，对三类文本条件编辑方法——源锚定、结构化提示和配对训练——进行了身份保持的显式测量。结果表明，所有方法均生成高质量的OCT图像且编辑成功率相近，但身份保持能力差异显著：源锚定方法频繁改变被拍摄者身份，而配对训练方法保留最佳。我们主张未来医学反事实生成研究必须显式测量并报告身份保持情况，与图像真实性和编辑效果并重。
原文摘要 · Abstract (English)
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.
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