PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling

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. While current hardware and machine learning algorithms can locate and type objects at scale, the manual assignment and review of large labeled datasets used to train or validate models remains arduous. For example, a single WSI may contain over 1 million cells, which, if requiring a modest 1 second per cell to label, would result in approximately 12 non-stop days of effort. To aid experts (e.g., pathologists) in this labeling process, several image analysis algorithms have been proposed PS is a user-friendly, browser-based tool, which allows the user to leverage deep learning (DL) to quickly review and apply labels at a group, as opposed to a single object, level (Figure 1).