放射科医生阅读的世界模型用于医学图像表示学习
A World Model of Radiologist Reading for Medical Image Representation Learning
AI总结：
提出GazeWorld，一种将图像视为世界、放射科医生注视序列视为轨迹的医学成像世界模型，通过自回归预测注视补丁表示和空间补全未访问区域，在多个基准上实现最先进的诊断准确率和零样本性能。
摘要：
放射科医生的眼动追踪数据提供了专家在图像阅读过程中如何搜索、比较和积累证据的丰富记录；然而，现有方法仅部分利用这一信号，要么作为静态空间先验，要么作为与诊断脱节的辅助预测目标。我们提出GazeWorld，一种医学成像世界模型，将图像视为世界，将放射科医生的注视序列视为通过该世界的轨迹。GazeWorld自回归地从所有先前访问过的补丁预测下一个注视补丁的潜在表示，同时一个空间补全分支覆盖未访问区域。在推理时，GazeWorld仅从图像生成一系列补丁表示，无需真实注视数据。冻结的GazeWorld特征在CheXpert、RSNA肺炎和SIIM-ACR气胸的所有九个监督设置中实现了最先进的诊断准确率，并在所有三个基准上取得了最高的零样本准确率。在GazeSearch基准上，使用相同冻结特征训练的通用解码器在ScanMatch和SED上分别比专门构建的LogitGaze-Med高出16%和22%，尽管未明确训练以预测注视。GazeWorld表明，建模专家如何阅读（而不仅仅是他们得出什么结论）为医学成像AI提供了一种有前景的预训练范式。
英文摘要：
Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading; yet, existing methods exploit this signal only partially, either as a static spatial prior or as an auxiliary prediction target decoupled from diagnosis. We propose GazeWorld, a medical imaging world model that treats the image as the world and the radiologist's fixation sequence as a trajectory through it. GazeWorld autoregressively predicts the latent representation of the next fixated patch from all previously visited ones, while a spatial-completion branch covers unvisited regions. At inference, GazeWorld generates a sequence of patch representations from the image alone without requiring real gaze data. Frozen GazeWorld features achieve state-of-the-art diagnostic accuracy across all nine supervised settings on CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax, as well as the highest zero-shot accuracy on all three benchmarks. On the GazeSearch benchmark, a generic decoder trained on the same frozen features outperforms the purpose-built LogitGaze-Med by over 16\% in ScanMatch and 22\% in SED, despite not being explicitly trained to predict gaze. GazeWorld demonstrates that modeling how experts read, not just what they conclude, offers a promising pretraining paradigm for medical imaging AI.
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。
