{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T16:20:17Z","timestamp":1759940417925,"version":"3.37.3"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2022,5,9]],"date-time":"2022-05-09T00:00:00Z","timestamp":1652054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"NIHR HTA","award":["DS406118"],"award-info":[{"award-number":["DS406118"]}]},{"name":"PathLAKE consortium","award":["104689","18181"],"award-info":[{"award-number":["104689","18181"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Digitization of pathology laboratories through digital slide scanners and advances in deep learning approaches for objective histological assessment have resulted in rapid progress in the field of computational pathology (CPath) with wide-ranging applications in medical and pharmaceutical research as well as clinical workflows. However, the estimation of robustness of CPath models to variations in input images is an open problem with a significant impact on the downstream practical applicability, deployment and acceptability of these approaches. Furthermore, development of domain-specific strategies for enhancement of robustness of such models is of prime importance as well.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this work, we propose the first domain-specific Robustness Evaluation and Enhancement Toolbox (REET) for computational pathology applications. It provides a suite of algorithmic strategies for enabling robustness assessment of predictive models with respect to specialized image transformations such as staining, compression, focusing, blurring, changes in spatial resolution, brightness variations, geometric changes as well as pixel-level adversarial perturbations. Furthermore, REET also enables efficient and robust training of deep learning pipelines in computational pathology. Python implementation of REET is available at https:\/\/github.com\/alexjfoote\/reetoolbox.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac315","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T19:15:44Z","timestamp":1651605344000},"page":"3312-3314","source":"Crossref","is-referenced-by-count":9,"title":["REET: robustness evaluation and enhancement toolbox for computational pathology"],"prefix":"10.1093","volume":"38","author":[{"given":"Alex","family":"Foote","sequence":"first","affiliation":[{"name":"Tissue Image Analytics Centre, Department of Computer Science, University of Warwick , Coventry CV4 7AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4424-3093","authenticated-orcid":false,"given":"Amina","family":"Asif","sequence":"additional","affiliation":[{"name":"Tissue Image Analytics Centre, Department of Computer Science, University of Warwick , Coventry CV4 7AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nasir","family":"Rajpoot","sequence":"additional","affiliation":[{"name":"Tissue Image Analytics Centre, Department of Computer Science, University of Warwick , Coventry CV4 7AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9129-1189","authenticated-orcid":false,"given":"Fayyaz","family":"Minhas","sequence":"additional","affiliation":[{"name":"Tissue Image Analytics Centre, Department of Computer Science, University of Warwick , Coventry CV4 7AL, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,5,9]]},"reference":[{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1111\/joim.13030","article-title":"Artificial intelligence as the next step towards precision pathology","volume":"288","author":"Acs","year":"2020","journal-title":"J. Intern. Med"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.3390\/electronics10172132","article-title":"A survey on adversarial deep learning robustness in medical image analysis","volume":"10","author":"Apostolidis","year":"2021","journal-title":"Electronics"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"e763","DOI":"10.1016\/S2589-7500(21)00180-1","article-title":"Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study","volume":"3","author":"Bilal","year":"2021","journal-title":"Lancet. Digit. Health"},{"year":"2021","author":"Foote","key":"2023041408200409300_"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1038\/s43018-020-0085-8","article-title":"Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis","volume":"1","author":"Fu","year":"2020","journal-title":"Nat. Cancer"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1038\/s43018-020-0087-6","article-title":"Pan-cancer image-based detection of clinically actionable genetic alterations","volume":"1","author":"Kather","year":"2020","journal-title":"Nat. Cancer"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","article-title":"Deep learning in histopathology: the path to the clinic","volume":"27","author":"van der Laak","year":"2021","journal-title":"Nat. Med"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41586-021-03512-4","article-title":"AI-based pathology predicts origins for cancers of unknown primary","volume":"594","author":"Lu","year":"2021","journal-title":"Nature"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1158\/1538-7445.AM2020-2105","article-title":"Abstract 2105: HE2RNA: a deep learning model for transcriptomic learning from digital pathology","volume":"80","author":"Pronier","year":"2020","journal-title":"Cancer Res"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1136\/jclinpath-2020-206908","article-title":"Current and future applications of artificial intelligence in pathology: a clinical perspective","volume":"74","author":"Rakha","year":"2021","journal-title":"J. Clin. Pathol"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"2098","DOI":"10.1038\/s41379-021-00859-x","article-title":"Quality control stress test for deep learning-based diagnostic model in digital pathology","volume":"34","author":"Sch\u00f6mig-Markiefka","year":"2021","journal-title":"Mod. Pathol"},{"year":"2019","author":"Shafahi","key":"2023041408200409300_"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/S0140-6736(19)32998-8","article-title":"Deep learning for prediction of colorectal cancer outcome: a discovery and validation study","volume":"395","author":"Skrede","year":"2020","journal-title":"The Lancet"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"101813","DOI":"10.1016\/j.media.2020.101813","article-title":"Deep neural network models for computational histopathology: a survey","volume":"67","author":"Srinidhi","year":"2021","journal-title":"Med. Image Anal"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"188452","DOI":"10.1016\/j.bbcan.2020.188452","article-title":"Closing the translation gap: AI applications in digital pathology","volume":"1875","author":"Steiner","year":"2021","journal-title":"Biochim. Biophys. Acta Rev. Cancer"},{"key":"2023041408200409300_","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/jop.13042","article-title":"The use of artificial intelligence, machine learning and deep learning in oncologic histopathology","volume":"49","author":"Sultan","year":"2020","journal-title":"J. Oral Pathol. Med"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac315\/43723893\/btac315.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/12\/3312\/49885164\/btac315.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/12\/3312\/49885164\/btac315.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:11:36Z","timestamp":1700525496000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/12\/3312\/6582557"}},"subtitle":[],"editor":[{"given":"Hanchuan","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,5,9]]},"references-count":16,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,6,13]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac315","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2022,6,15]]},"published":{"date-parts":[[2022,5,9]]}}}