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
Published inNPJ digital medicine Vol. 7; no. 1; pp. 164 - 7
Main Authors Walker, Cédric, 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 London Nature Publishing Group UK 20.06.2024
Nature Publishing Group
Nature Portfolio
Subjects
631/114/1305
631/114/1564
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Accuracy
Algorithms
Biomarkers
Biomedical engineering
Biomedicine
Biotechnology
Brief Communication
Datasets
Deep learning
Efficiency
Interfaces
Labeling
Medicine
Medicine & Public Health
Oncology
Pathology
Subject specialists
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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.
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ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-024-01150-4