@article{3df252b4f0fe4777965986dcf253a6ee,
title = "Open and Reusable Deep Learning for Pathology with WSInfer and QuPath",
abstract = "Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer: an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology.",
author = "Kaczmarzyk, {Jakub R} and Alan O'Callaghan and Fiona Inglis and Swarad Gat and Tahsin Kurc and Rajarsi Gupta and Erich Bremer and Peter Bankhead and Saltz, {Joel H.}",
note = "Funding Information: The development of the WSinfer infrastructure by the Stony Brook authors was supported by Stony Brook Provost ProFund 2022 award and through the generosity of Bob Beals and Betsy Barton. JRK was also supported by the National Institutes of Health grant T32GM008444 (NIGMS) and by the Medical Scientist Training Program at Stony Brook University. The QuPath WSInfer extension was developed by the Edinburgh authors and was made possible in part by grant number 2021-237595 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. This research was funded in part by the Wellcome Trust 223750/Z/21/Z. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Author Accepted Manuscript version arising from this submission. Publisher Copyright: {\textcopyright} 2024, The Author(s).",
year = "2024",
month = jan,
day = "10",
doi = "10.1038/s41698-024-00499-9",
language = "English",
journal = "npj Precision Oncology ",
issn = "2397-768X",
publisher = "Springer Nature",
}