PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling
This tool addresses the need for high-throughput labeling in digital pathology, enabling faster discovery of patterns for diagnosis, prognosis, and therapy response, though it is incremental as it builds on existing deep learning methods.
The researchers tackled the problem of labeling large quantities of histological objects in digital pathology images by releasing PatchSorter, an open-source tool that integrates deep learning with a web interface, achieving over a 7x improvement in labels per second with minimal impact on accuracy.
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.