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

• URL: http://arxiv.org/abs/2307.07528v1
• Date: Thu, 13 Jul 2023 09:32:42 GMT
• Title: PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling
• Authors: Cedric Walker, Tasneem Talawalla, Robert Toth, Akhil Ambekar, Kien Rea, Oswin Chamian, Fan Fan, Sabina Berezowska, Sven Rottenberg, Anant Madabhushi, Marie Maillard, Laura Barisoni, Hugo Mark Horlings, Andrew Janowczyk
• Abstract summary: We release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. We demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy.
• Score: 0.8290040611295051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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