利用观察者间眼动一致性支持有丝分裂检测AI训练
Supporting Mitosis Detection AI Training with Inter-Observer Eye-Gaze Consistencies
AI总结：
本研究提出利用观察者间一致的眼动注视点生成低成本训练标签，用于有丝分裂检测AI训练，实验表明其性能接近真实标注并显著优于启发式基线。
摘要：
人工智能（AI）在病理学任务中的扩展加剧了AI开发中对医生标注的需求。然而，从医生处收集高质量标注既昂贵又耗时，成为AI进展中的瓶颈。本研究探讨了眼动追踪作为一种具有成本效益的技术，用于收集医生的行为数据以训练AI，重点关注有丝分裂检测这一病理学任务。使用眼动数据的一个主要挑战是信噪比低，这阻碍了有意义信息的提取。我们通过利用观察者间眼动一致性的特性，并基于一组观察者共享的一致眼动注视点来生成眼动标签，从而解决了这一问题。本研究涉及14名非医学背景的参与者，我们收集了他们的眼动数据，并根据不同的组规模生成了眼动标签。我们通过训练卷积神经网络（CNNs）评估了这些眼动标签的有效性，并将其性能与使用真实标注训练的CNN以及基于启发式的基线方法进行了比较。结果表明，使用我们的眼动标签训练的CNN在性能上接近基于真实标注的CNN，并显著优于基线方法。尽管主要聚焦于有丝分裂检测，我们预计本研究的见解可以推广到其他医学成像任务。
英文摘要：
The expansion of artificial intelligence (AI) in pathology tasks has intensified the demand for doctors' annotations in AI development. However, collecting high-quality annotations from doctors is costly and time-consuming, creating a bottleneck in AI progress. This study investigates eye-tracking as a cost-effective technology to collect doctors' behavioral data for AI training with a focus on the pathology task of mitosis detection. One major challenge in using eye-gaze data is the low signal-to-noise ratio, which hinders the extraction of meaningful information. We tackled this by levering the properties of inter-observer eye-gaze consistencies and creating eye-gaze labels from consistent eye-fixations shared by a group of observers. Our study involved 14 non-medical participants, from whom we collected eye-gaze data and generated eye-gaze labels based on varying group sizes. We assessed the efficacy of such eye-gaze labels by training Convolutional Neural Networks (CNNs) and comparing their performance to those trained with ground truth annotations and a heuristic-based baseline. Results indicated that CNNs trained with our eye-gaze labels closely followed the performance of ground-truth-based CNNs, and significantly outperformed the baseline. Although primarily focused on mitosis, we envision that insights from this study can be generalized to other medical imaging tasks.
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