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arXiv Open Access 2023

PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling

Cedric Walker Tasneem Talawalla Robert Toth Akhil Ambekar Kien Rea +9 lainnya
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Abstrak

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.

Topik & Kata Kunci

q-bio.QM cs.AI cs.CV cs.HC eess.IV

Penulis (14)

C

Cedric Walker

T

Tasneem Talawalla

R

Robert Toth

A

Akhil Ambekar

K

Kien Rea

O

Oswin Chamian

F

Fan Fan

S

Sabina Berezowska

S

Sven Rottenberg

A

Anant Madabhushi

M

Marie Maillard

L

Laura Barisoni

H

Hugo Mark Horlings

A

Andrew Janowczyk

Format Sitasi

APA MLA BibTeX
Walker, C., Talawalla, T., Toth, R., Ambekar, A., Rea, K., Chamian, O. et al. (2023). PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling. https://arxiv.org/abs/2307.07528

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Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓