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

delete2024-06-20
delete0
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OA
AI
C
C.F. Walker
T
Tasneem Talawalla
R
Róbert Tóth
A
Akhil Ambekar
K
Kien Rea
O
Oswin Chamian
樊凡 封面图
樊凡 (Fan Fan)
S
Sabina Berezowska
S
Sven Rottenberg
A
Anant Madabhushi
M
Marie Maillard
L
Laura Barisoni
H
Hugo M. Horlings
A
Andrew Janowczyk *
DOI：10.1038/s41746-024-01150-4delete
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摘要

摘要

En 中文
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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期刊

npj Digital Medicine 封面图
npj Digital Medicine
IF:
15.1
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3.3K
被引数:
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Duke University
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Case Western Reserve University
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University of Bern
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4.0W
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被引数: 4.8W
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