IEEE J Biomed Health Inform. 2022 Jul;26(7):3025-3036. doi: 10.1109/JBHI.2022.3148944. Epub 2022 Jul 1.
Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process. This work explores the viability and timing comparisons of eye gaze labeling compared to conventional manual labeling for training object detectors. Challenges associated with gaze based labeling and methods to refine the coarse data annotations for subsequent object detection are also discussed. Results demonstrate that gaze tracking based labeling can save valuable pathologist time and delivers good performance when employed for training a deep object detector. Using the task of localization of Keratin Pearls in cases of oral squamous cell carcinoma as a test case, we compare the performance gap between deep object detectors trained using hand-labelled and gaze-labelled data. On average, compared to 'Bounding-box' based hand-labeling, gaze-labeling required 57.6% less time per label and compared to 'Freehand' labeling, gaze-labeling required on average 85% less time per label.
大型训练数据集的缺乏是一个需要克服的瓶颈，才能实现深度学习在组织病理学应用中的真正潜力。虽然通过全切片成像扫描仪进行幻灯片数字化已经提高了数据采集的速度，但虚拟幻灯片的标记仍然需要病理学家大量的时间投入。眼动注视标注有可能加速幻灯片标记过程。这项工作探讨了眼动注视标注与传统手动标注在训练目标探测器方面的可行性和时间比较。还讨论了与基于注视的标注相关的挑战以及用于后续目标检测的粗粒度数据标注的改进方法。结果表明，当用于训练深度目标探测器时，基于注视跟踪的标注可以节省宝贵的病理学家时间并提供良好的性能。我们使用口腔鳞状细胞癌病例中角蛋白珠定位的任务作为测试案例，比较了使用手工标注和注视标注数据训练的深度目标探测器之间的性能差距。平均而言，与基于“边界框”的手工标注相比，注视标注每个标签所需的时间减少了 57.6%，与“徒手”标注相比，注视标注每个标签所需的时间平均减少了 85%。
