{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T22:36:17Z","timestamp":1725575777750},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000092","name":"U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine","doi-asserted-by":"publisher","award":["1R01LM013864"],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["1U01DK133090","2R01DK118431-04","U54DK083912"],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases"},{"name":"U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases"},{"DOI":"10.13039\/100006108","name":"U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["U2CTR002818"],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"Abstract<\/jats:title>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.<\/jats:p>","DOI":"10.1038\/s41746-024-01150-4","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T17:02:54Z","timestamp":1718902974000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PatchSorter: a high throughput deep learning digital pathology tool for object labeling"],"prefix":"10.1038","volume":"7","author":[{"ORCID":"http:\/\/orcid.org\/0009-0001-1508-2083","authenticated-orcid":false,"given":"C\u00e9dric","family":"Walker","sequence":"first","affiliation":[]},{"given":"Tasneem","family":"Talawalla","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Toth","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6241-6234","authenticated-orcid":false,"given":"Akhil","family":"Ambekar","sequence":"additional","affiliation":[]},{"given":"Kien","family":"Rea","sequence":"additional","affiliation":[]},{"given":"Oswin","family":"Chamian","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Sabina","family":"Berezowska","sequence":"additional","affiliation":[]},{"given":"Sven","family":"Rottenberg","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5741-0399","authenticated-orcid":false,"given":"Anant","family":"Madabhushi","sequence":"additional","affiliation":[]},{"given":"Marie","family":"Maillard","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0848-9683","authenticated-orcid":false,"given":"Laura","family":"Barisoni","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4782-8828","authenticated-orcid":false,"given":"Hugo Mark","family":"Horlings","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2982-4321","authenticated-orcid":false,"given":"Andrew","family":"Janowczyk","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"1150_CR1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-018-4448-9","volume":"18","author":"J Whitney","year":"2018","unstructured":"Whitney, J. et al. Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. BMC Cancer 18, 610 (2018).","journal-title":"BMC Cancer"},{"key":"1150_CR2","doi-asserted-by":"publisher","first-page":"eabn3966","DOI":"10.1126\/sciadv.abn3966","volume":"8","author":"X Wang","year":"2022","unstructured":"Wang, X. et al. Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. Sci. Adv. 8, eabn3966 (2022).","journal-title":"Sci. Adv."},{"key":"1150_CR3","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1158\/1078-0432.CCR-19-2659","volume":"26","author":"HK Bhargava","year":"2020","unstructured":"Bhargava, H. K. et al. Computationally derived image signature of stromal morphology is prognostic of prostate cancer recurrence following prostatectomy in African American patients. Clin. Cancer Res. 26, 1915\u20131923 (2020).","journal-title":"Clin. Cancer Res."},{"key":"1150_CR4","doi-asserted-by":"publisher","first-page":"101859","DOI":"10.1016\/j.media.2020.101859","volume":"67","author":"P Pati","year":"2021","unstructured":"Pati, P., Foncubierta-Rodr\u00edguez, A., Goksel, O. & Gabrani, M. Reducing annotation effort in digital pathology: a co-representation learning framework for classification tasks. Med. Image Anal. 67, 101859 (2021).","journal-title":"Med. Image Anal."},{"key":"1150_CR5","doi-asserted-by":"crossref","unstructured":"Bengar, J. Z., van de Weijer, J., Twardowski, B. & Raducanu, B. Reducing label effort: self-supervised meets active learning. In Proc. IEEE\/CVF International Conference on Computer Vision 1631\u20131639 (IEEE, 2021).","DOI":"10.1109\/ICCVW54120.2021.00188"},{"key":"1150_CR6","doi-asserted-by":"crossref","unstructured":"Menon, A., Singh, P., Vinod, P. K. & Jawahar, C. V. Interactive learning for assisting whole slide image annotation. In Pattern Recognition 504\u2013517 (Springer, 2022).","DOI":"10.1007\/978-3-031-02444-3_38"},{"key":"1150_CR7","doi-asserted-by":"publisher","unstructured":"Holub, A., Perona, P. & Burl, M. C. Entropy-based active learning for object recognition. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 1\u20138 https:\/\/doi.org\/10.1109\/CVPRW.2008.4563068 (IEEE, 2008).","DOI":"10.1109\/CVPRW.2008.4563068"},{"key":"1150_CR8","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1038\/s42256-019-0018-3","volume":"1","author":"B Lutnick","year":"2019","unstructured":"Lutnick, B. et al. An integrated iterative annotation technique for easing neural network training in medical image analysis. Nat. Mach. Intell. 1, 112\u2013119 (2019).","journal-title":"Nat. Mach. Intell."},{"key":"1150_CR9","doi-asserted-by":"publisher","first-page":"101805","DOI":"10.1016\/j.artmed.2020.101805","volume":"103","author":"A Das","year":"2020","unstructured":"Das, A., Nair, M. S. & Peter, D. S. Batch mode active learning on the Riemannian manifold for automated scoring of nuclear pleomorphism in breast cancer. Artif. Intell. Med. 103, 101805 (2020).","journal-title":"Artif. Intell. Med."},{"key":"1150_CR10","doi-asserted-by":"publisher","first-page":"27","DOI":"10.4103\/jpi.jpi_5_20","volume":"11","author":"M LindvaN","year":"2020","unstructured":"LindvaN, M. et al. TissueWand, a rapid histopathology annotation tool. J. Pathol. Inform. 11, 27 (2020).","journal-title":"J. Pathol. Inform."},{"key":"1150_CR11","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1158\/1078-0432.CCR-18-2013","volume":"25","author":"G Corredor","year":"2019","unstructured":"Corredor, G. et al. Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clin. Cancer Res. 25, 1526\u20131534 (2019).","journal-title":"Clin. Cancer Res."},{"key":"1150_CR12","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y. & Deny, S. Barlow Twins: Self-Supervised Learning via Redundancy Reduction. in Proceedings of the 38th International Conference on Machine Learning (eds. Meila, M. & Zhang, T.) vol. 139, 12310\u201312320 (PMLR, 2021)."},{"key":"1150_CR13","unstructured":"Baldi, P. Autoencoders, unsupervised learning, and deep architectures. In Proc. ICML Workshop on Unsupervised and Transfer Learning 27 37\u201349 (PMLR, 2012)."},{"key":"1150_CR14","unstructured":"Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A Simple Framework for Contrastive Learning of Visual Representations. in Proceedings of the 37th International Conference on Machine Learning (eds. III, H. D. & Singh, A.) vol. 119, 1597\u20131607 (PMLR, 2020)."},{"key":"1150_CR15","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770\u2013778 (IEEE, 2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1150_CR16","doi-asserted-by":"publisher","unstructured":"McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1802.03426 (2020).","DOI":"10.48550\/arXiv.1802.03426"},{"key":"1150_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41523-021-00346-1","volume":"7","author":"K El Bairi","year":"2021","unstructured":"El Bairi, K. et al. The tale of TILs in breast cancer: a report from The International Immuno-Oncology Biomarker Working Group. Npj Breast Cancer 7, 1\u201317 (2021).","journal-title":"Npj Breast Cancer"},{"key":"1150_CR18","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1093\/annonc\/mdu450","volume":"26","author":"R Salgado","year":"2015","unstructured":"Salgado, R. et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann. Oncol. 26, 259\u2013271 (2015).","journal-title":"Ann. Oncol."},{"key":"1150_CR19","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ejca.2018.07.013","volume":"102","author":"AGJ van Rossum","year":"2018","unstructured":"van Rossum, A. G. J. et al. Adjuvant dose-dense doxorubicin-cyclophosphamide versus docetaxel-doxorubicin-cyclophosphamide for high-risk breast cancer: first results of the randomised MATADOR trial (BOOG 2004-04). Eur. J. Cancer 102, 40\u201348 (2018).","journal-title":"Eur. J. Cancer"},{"key":"1150_CR20","doi-asserted-by":"publisher","first-page":"1962","DOI":"10.1109\/TMI.2016.2529665","volume":"35","author":"A Vahadane","year":"2016","unstructured":"Vahadane, A. et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35, 1962\u20131971 (2016).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1150_CR21","doi-asserted-by":"publisher","first-page":"101563","DOI":"10.1016\/j.media.2019.101563","volume":"58","author":"S Graham","year":"2019","unstructured":"Graham, S. et al. Hover-Net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med. Image Anal. 58, 101563 (2019).","journal-title":"Med. Image Anal."},{"key":"1150_CR22","first-page":"117","volume":"156","author":"G Jaume","year":"2021","unstructured":"Jaume, G., Gabrani, M., Pati, P., Anklin, V. & Foncubierta, A. HistoCartography: a toolkit for graph analytics in digital pathology. MICCAI Workshop Comput. Pathol. 156, 117\u2013128 (2021).","journal-title":"MICCAI Workshop Comput. Pathol."},{"key":"1150_CR23","doi-asserted-by":"publisher","first-page":"937","DOI":"10.3892\/mmr.2012.1048","volume":"6","author":"R Masuda","year":"2012","unstructured":"Masuda, R. et al. Tumor budding is a significant indicator of a poor prognosis in lung squamous cell carcinoma patients. Mol. Med. Rep. 6, 937\u2013943 (2012).","journal-title":"Mol. Med. Rep."},{"key":"1150_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015 234\u2013241 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1150_CR25","doi-asserted-by":"publisher","first-page":"F1526","DOI":"10.1152\/ajprenal.00459.2019","volume":"317","author":"M Polesel","year":"2019","unstructured":"Polesel, M. & Hall, A. M. Axial differences in endocytosis along the kidney proximal tubule. Am. J. Physiol. Ren. Physiol. 317, F1526\u2013F1530 (2019).","journal-title":"Am. J. Physiol. Ren. Physiol."},{"key":"1150_CR26","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/ki.2012.428","volume":"83","author":"CA Gadegbeku","year":"2013","unstructured":"Gadegbeku, C. A. et al. Design of the nephrotic syndrome study network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach. Kidney Int. 83, 749\u2013756 (2013).","journal-title":"Kidney Int."},{"key":"1150_CR27","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-17204-5","volume":"7","author":"P Bankhead","year":"2017","unstructured":"Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).","journal-title":"Sci. Rep."},{"key":"1150_CR28","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/j.kint.2017.01.002","volume":"91","author":"S Sethi","year":"2017","unstructured":"Sethi, S. et al. A proposal for standardized grading of chronic changes in native kidney biopsy specimens. Kidney Int. 91, 787\u2013789 (2017).","journal-title":"Kidney Int."},{"key":"1150_CR29","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1016\/j.kint.2017.09.028","volume":"93","author":"MS Hommos","year":"2018","unstructured":"Hommos, M. S. et al. Global glomerulosclerosis with nephrotic syndrome; the clinical importance of age adjustment. Kidney Int. 93, 1175\u20131182 (2018).","journal-title":"Kidney Int."},{"key":"1150_CR30","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1053\/j.ajkd.2018.07.020","volume":"73","author":"LH Mariani","year":"2019","unstructured":"Mariani, L. H. et al. CureGN study rationale, design, and methods: establishing a large prospective observational study of glomerular disease. Am. J. Kidney Dis. 73, 218\u2013229 (2019).","journal-title":"Am. J. Kidney Dis."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01150-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01150-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01150-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T17:05:43Z","timestamp":1718903143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01150-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,20]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1150"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01150-4","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,20]]},"assertion":[{"value":"24 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A.M. is an equity holder in Picture Health, Elucid Bioimaging, and Inspirata Inc. Currently, he serves on the advisory board of Picture Health, Aiforia Inc., and SimBioSys. He also currently consults for SimBioSys. He also has sponsored research agreements with AstraZeneca, Boehringer-Ingelheim, Eli-Lilly, and Bristol Myers-Squibb. His technology has been licensed to Picture Health and Elucid Bioimaging. He is also involved in three different R01 grants with Inspirata Inc. L.B. is a consultant for Sangamo and Protalix and is on the scientific advisory boards of Vertex and Nephcure. A.J. provides consulting for Merck, Lunaphore, and Roche, the latter of which he also has a sponsored research agreement. H.M.H. received financial compensation from Roche Diagnostics BV paid to the institute. No other conflicts of interest were declared.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"164"}}