{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T20:44:05Z","timestamp":1760647445230,"version":"3.37.3"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2021,6,1]],"date-time":"2021-06-01T00:00:00Z","timestamp":1622505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not. As all tiles within a tumor free slide are tumor free, these can be used to capture tumor-free patterns using the one-class learning strategy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present a Python package, termed OCTID, which combines a pretrained convolutional neural network (CNN) model,\u00a0Uniform Manifold Approximation and Projection (UMAP) and one-class support vector machine to achieve accurate tumor tile classification using a training set of tumor free tiles. Benchmarking experiments on four H&amp;E image datasets achieved remarkable performance in terms of F1-score (0.90\u2009\u00b1\u20090.06), Matthews correlation coefficient (0.93\u2009\u00b1\u20090.05) and accuracy (0.94\u2009\u00b1\u20090.03).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Detailed information can be found in the Supplementary File.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab416","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T12:46:35Z","timestamp":1622119595000},"page":"3986-3988","source":"Crossref","is-referenced-by-count":10,"title":["OCTID: a one-class learning-based Python package for tumor image detection"],"prefix":"10.1093","volume":"37","author":[{"given":"Yanan","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University , VIC 3800, Australia"}]},{"given":"Litao","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Monash e-Research Centre, Monash University , VIC 3800, Australia"}]},{"given":"Geoffrey I","family":"Webb","sequence":"additional","affiliation":[{"name":"Department of Data Science and Artificial Intelligence, Monash Data Futures Institute, Monash University , VIC 3800, Australia"}]},{"given":"Zongyuan","family":"Ge","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Monash e-Research Centre, Monash University , VIC 3800, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8031-9086","authenticated-orcid":false,"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University , VIC 3800, Australia"}]}],"member":"286","published-online":{"date-parts":[[2021,6,1]]},"reference":[{"key":"2023051607343896400_btab416-B1","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.5858\/arpa.2019-0229-ED","article-title":"Regulating artificial intelligence for a successful pathology future","volume":"143","author":"Allen","year":"2019","journal-title":"Arch. 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Res"},{"key":"2023051607343896400_btab416-B9","doi-asserted-by":"crossref","first-page":"861","DOI":"10.21105\/joss.00861","article-title":"Umap: uniform manifold approximation and projection","volume":"3","author":"McInnes","year":"2018","journal-title":"J. Open Source Softw"},{"key":"2023051607343896400_btab416-B10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-20030-5","article-title":"Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images","volume":"11","author":"Noorbakhsh","year":"2020","journal-title":"Nat. Commun"},{"year":"2019","author":"Paszke","key":"2023051607343896400_btab416-B11"},{"key":"2023051607343896400_btab416-B12","doi-asserted-by":"crossref","first-page":"5450","DOI":"10.1109\/TIP.2019.2917862","article-title":"Learning deep features for one-class classification","volume":"28","author":"Perera","year":"2019","journal-title":"IEEE Transact. Image Process"},{"key":"2023051607343896400_btab416-B13","doi-asserted-by":"crossref","first-page":"2374289519873088","DOI":"10.1177\/2374289519873088","article-title":"Artificial intelligence and machine learning in pathology: the present landscape of supervised methods","volume":"6","author":"Rashidi","year":"2019","journal-title":"Acad. 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