PatchSorter: a high throughput deep learning digital pathology tool for object labeling

• Published in: NPJ 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
• Language: English
• Published: London Nature Publishing Group UK 20.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; 692/53; Accuracy; Algorithms; Biomarkers; Biomedical engineering; Biomedicine; Biotechnology; Brief Communication; Datasets; Deep learning; Efficiency; Interfaces; Labeling; Medicine; Medicine & Public Health; Oncology; Pathology; Subject specialists

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...