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 (2024). PatchSorter: a high throughput deep learning digital pathology tool for object labeling. NPJ digital medicine, 7(1) Nature Publishing Group 10.1038/s41746-024-01150-4
|
Text
s41746-024-01150-4.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview |
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.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
05 Veterinary Medicine > Department of Infectious Diseases and Pathobiology (DIP) > Institute of Animal Pathology 05 Veterinary Medicine > Department of Infectious Diseases and Pathobiology (DIP) 04 Faculty of Medicine > Faculty Institutions > Bern Center for Precision Medicine (BCPM) |
Graduate School: |
Graduate School for Cellular and Biomedical Sciences (GCB) |
UniBE Contributor: |
Walker, Cédric André, Rottenberg, Sven |
Subjects: |
600 Technology > 610 Medicine & health 600 Technology > 630 Agriculture |
ISSN: |
2398-6352 |
Publisher: |
Nature Publishing Group |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
26 Jun 2024 10:35 |
Last Modified: |
26 Jun 2024 10:35 |
Publisher DOI: |
10.1038/s41746-024-01150-4 |
PubMed ID: |
38902336 |
BORIS DOI: |
10.48350/197981 |
URI: |
https://boris.unibe.ch/id/eprint/197981 |
Actions (login required)
 |
Edit item |