收集19位病理医生读取397张病理切片时的眼动、鼠标和决策数据，揭示诊断行为规律。
Eye-Tracking, Mouse Tracking, Stimulus Tracking,and Decision-Making Datasets in Digital Pathology
- 通过眼动+鼠标+操作轨迹追踪，记录完整诊断流程
- 共采集17万次注视、26万次眼跳和186万次鼠标操作
- 数据可提升医生培训与AI辅助诊断系统性能
解读千兆像素全切片图像（WSIs）是病理医生的重要但困难任务，诊断准确率平均仅约70%，增加第二位医生也无法显著提高判断一致性。该领域缺乏解释诊断错误与不一致性的行为数据。为填补空白，我们发布了PathoGaze1.0，一个综合性行为数据集，捕捉19位病理医生在癌症诊断全流程中的动态视觉搜索与决策过程。数据集包含18.69小时的眼动、鼠标交互、刺激物追踪、视口导航及诊断决策数据（EMSVD），来自397张WSIs的分析。数据采集基于应用导向的测试环境PTAH，确保生态有效性。共记录171,909次注视、263,320次扫视和1,867,362次鼠标交互事件。这些数据还可用于改进医生培训与支持人类专家的AI系统训练。所有实验均在https://osf.io/hj9a7预先注册，完整数据集与分析代码见https://go.osu.edu/pathogaze。
原文摘要 · Abstract (English)
Interpretation of giga-pixel whole-slide images (WSIs) is an important but difficult task for pathologists. Their diagnostic accuracy is estimated to average around 70%. Adding a second pathologist does not substantially improve decision consistency. The field lacks adequate behavioral data to explain diagnostic errors and inconsistencies. To fill in this gap, we present PathoGaze1.0, a comprehensive behavioral dataset capturing the dynamic visual search and decision-making processes of the full diagnostic workflow during cancer diagnosis. The dataset comprises 18.69 hours of eye-tracking, mouse interaction, stimulus tracking, viewport navigation, and diagnostic decision data (EMSVD) collected from 19 pathologists interpreting 397 WSIs. The data collection process emphasizes ecological validity through an application-grounded testbed, called PTAH. In total, we recorded 171,909 fixations, 263,320 saccades, and 1,867,362 mouse interaction events. In addition, such data could also be used to improve the training of both pathologists and AI systems that might support human experts. All experiments were preregistered at https://osf.io/hj9a7, and the complete dataset along with analysis code is available at https://go.osu.edu/pathogaze.
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