论文标题
关于基于智能注视的组织病理学图像的注释，用于培训深卷积神经网络
On Smart Gaze based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks
论文作者
论文摘要
大型培训数据集的不可用是一种需要克服的瓶颈，以实现组织病理学应用中深度学习的真正潜力。尽管通过整个幻灯片成像扫描仪进行的幻灯片数字化提高了数据获取速度，但虚拟幻灯片的标签需要从病理学家那里进行大量时间投资。眼睛凝视注释有可能加快幻灯片标记过程。这项工作探讨了与训练对象探测器的常规手动标记相比，眼睛凝视标签的生存能力和计时比较。还讨论了与凝视基于凝视的标签和方法相关的挑战，以完善以后的对象检测的粗糙数据注释。结果表明，基于凝视跟踪的标签可以节省宝贵的病理学家时间，并在训练深度对象探测器时提供良好的性能。利用在口服鳞状细胞癌作为测试用例的角蛋白珍珠定位的任务中，我们比较了使用手工标记和注视标记的数据训练的深对象检测器之间的性能差距。平均而言，与基于“边界盒”的手持标签相比，注视标签所需的$ 57.6 \％$ $ $每个标签的时间更少，并且与“徒手”标签相比，平均需要$ 85 \％$ $ $ $ $。
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.
