从CT阅片时的三维注视模式预测放射科医生的专业水平
Predicting Radiologist Expertise from 3D Gaze Patterns During CT Interpretation
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中文总结 AI 辅助
该研究提出基于注视信息的Transformer框架，利用放射科医生CT阅片的三维注视模式，在182次阅片会话数据上实现0.91的ROC-AUC和0.86的F1分数，可客观评估放射科医生专业水平。
中文摘要 AI 辅助
准确解读容积CT需要高效浏览三维图像容积并关注诊断相关区域。尽管眼动追踪已在二维医学影像中得到广泛研究，但其在CT场景下用于专业水平评估的应用仍有限。我们提出一种基于注视信息的Transformer框架，用于胸部CT的专业水平分类。使用DINOv2作为骨干网络，放射科医生的注视点模式通过两种方式融入容积特征学习：（1）自注意力中的可学习对数空间偏置；（2）基于注视的patch嵌入池化。我们对5名不同经验水平放射科医生的182次CT阅片会话进行了训练和评估。在保留的测试集上，该模型的ROC-AUC达到0.91，F1分数为0.86，优于适配后的方法。这些发现表明，将视觉搜索行为融入Transformer可能支持放射学中基于过程的客观专业水平评估。代码可通过该https URL获取。
英文摘要
Accurate interpretation of volumetric CT requires efficient navigation of 3D image volumes and attention to diagnostically relevant regions. While eye-tracking has been widely studied in 2D medical imaging, its use for expertise assessment in CT settings remains limited. We propose a gaze-informed transformer framework for expertise classification in thoracic CT. Using a DINOv2 backbone, radiologist fixation patterns are integrated into volumetric feature learning through (1) a learnable log-space bias in self-attention and (2) gaze-weighted pooling of patch embeddings. We trained and evaluated our approach on 182 CT reading sessions from five radiologists with varying levels of experience. On a held-out test set, the model achieves an ROC-AUC of 0.91 and F1 score of 0.86, outperforming adapted methods. These findings suggest that incorporating visual search behavior into transformers may support objective, process-based expertise assessment in radiology. Code is available via https://github.com/leiluk1/GazeToSkill.
发表机构
- University of Copenhagen（哥本哈根大学）
- University of Iowa（爱荷华大学）
机构由 AI 辅助整理，请以论文原文为准。
