The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

翻译：数字病理图像中与诊断、预后及治疗反应相关的模式发现，通常需要对大量组织学对象进行繁琐的标注。本文发布了一款开源标注工具PatchSorter，该工具将深度学习与直观的网页界面相结合。通过使用超过10万个对象，我们证明其相比无辅助标注每秒标注量提升了7倍以上，且对标注准确性的影响极小，从而实现了对大型数据集的高通量标注。