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

TL;DR

PatchSorter is an open-source deep learning tool that significantly accelerates object labeling in digital pathology images, enabling high-throughput annotation with minimal accuracy loss.

Contribution

It introduces a novel high-throughput labeling tool that combines deep learning with an intuitive interface for large-scale digital pathology datasets.

Findings

Over 7x faster labeling than unaided methods

High accuracy maintained during rapid labeling

Successfully labeled over 100,000 objects

Abstract

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.