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.
@article{arxiv.2307.07528,
title = {PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling},
author = {Cedric Walker and Tasneem Talawalla and Robert Toth and Akhil Ambekar and Kien Rea and Oswin Chamian and Fan Fan and Sabina Berezowska and Sven Rottenberg and Anant Madabhushi and Marie Maillard and Laura Barisoni and Hugo Mark Horlings and Andrew Janowczyk},
journal= {arXiv preprint arXiv:2307.07528},
year = {2023}
}
Comments
The submission includes 15 pages, 8 figures, 1 table, and 30 references. It is a new submission