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 intuitiv...

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Bibliographic Details
Main Authors Walker, Cedric, Talawalla, Tasneem, Toth, Robert, Ambekar, Akhil, Rea, Kien, Chamian, Oswin, Fan, Fan, Berezowska, Sabina, Rottenberg, Sven, Madabhushi, Anant, Maillard, Marie, Barisoni, Laura, Horlings, Hugo Mark, Janowczyk, Andrew
Format Journal Article
LanguageEnglish
Published 13.07.2023
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Human-Computer Interaction
Quantitative Biology - Quantitative Methods
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Summary: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.
DOI:10.48550/arxiv.2307.07528