发表机构
Columbia University; Stanford University; Emory University（哥伦比亚大学; 斯坦福大学; 埃默里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对放射报告生成VLM的遗漏噪声问题，提出PU-DPO框架，将未提及项视为无标签，通过对比对优化，提升病理检测率与隐藏正例恢复能力，增强对遗漏噪声的鲁棒性。
AI 中文摘要
用于放射科报告生成的视觉-语言模型（VLMs）通常基于回顾性临床报告进行训练，这类报告存在遗漏噪声问题：由于细微发现被遗漏，临床上存在的异常表现未被记录。例如， prior研究显示，当成像请求聚焦于监测支持设备放置时，ICU胸部X射线报告可能会遗漏心脏增大（cardiomegaly）。因此，采用标准方法训练的模型会继承这些遗漏，自身也会学习到少报异常表现。我们提出PU-DPO，一种偏好优化框架，用于防止遗漏噪声破坏偏好信号。我们在正例-无标签（PU）学习框架下重新制定目标，将未提及的内容视为无标签而非真正的负例。我们的框架使用通过编辑模型响应生成的对比对提供偏好监督，生成明确提及或省略特定异常表现的变体。在视觉证据的背景下，提及该异常表现的生成响应自然更受偏好。在半合成实验和带有经裁定标签的真实世界胸部放射基准分析中，PU-DPO在多种病理的检测率和隐藏正例的恢复上取得了一致提升，且比现有方法对遗漏噪声更具鲁棒性。
英文摘要
Vision-Language Models (VLMs) for radiology report generation are typically trained on retrospective clinical reports, which suffer from omission noise: clinically present findings are left unreported due to the omission of subtle findings. For example, prior studies show that cardiomegaly may be omitted from ICU chest X-ray reports when the imaging request is focused on monitoring support device placement. As a result, models trained with standard approaches inherit these omissions, learning to under-report findings themselves. We propose PU-DPO, a preference optimization framework to prevent omission noise from corrupting the preference signal. We reformulate the objective under a positive-unlabeled (PU) learning framework, treating absent mentions as unlabeled rather than truly negative. Our framework provides preference supervision using constructed contrastive pairs, generated using edits to model responses, producing variants that explicitly mention or omit a specific finding. Generated responses that mention the finding are naturally preferred in the context of visual evidence. Across semi-synthetic experiments and analyses on real-world chest radiograph benchmarks where adjudicated labels are available, PU-DPO yields consistent gains in detection rates and recovery of hidden positives across multiple pathologies, and is more robust to omission noise than prior approaches.
