{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T22:31:03Z","timestamp":1751409063259,"version":"3.40.4"},"reference-count":77,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100009318","name":"Helmholtz Association under the Joint Research School \u201cMunich School for Data Science (MUDS)\u201d","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009318","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Helmholtz Association under the Joint Research School \u201cMUDS\u201d"},{"DOI":"10.13039\/100008662","name":"Joachim Herz Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005972","name":"Deutsche Krebshilfe","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005972","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tmi.2025.3532728","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T18:53:32Z","timestamp":1737572012000},"page":"2002-2015","source":"Crossref","is-referenced-by-count":1,"title":["Navigating Through Whole Slide Images With Hierarchy, Multi-Object, and Multi-Scale Data"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9431-754X","authenticated-orcid":false,"given":"Manuel","family":"Tran","sequence":"first","affiliation":[{"name":"Roche Diagnostics, Penzberg, Germany"}]},{"given":"Sophia","family":"Wagner","sequence":"additional","affiliation":[{"name":"Helmholtz Munich, Oberschleißheim, Germany"}]},{"given":"Wilko","family":"Weichert","sequence":"additional","affiliation":[{"name":"German Cancer Consortium, Munich, Germany"}]},{"given":"Christian","family":"Matek","sequence":"additional","affiliation":[{"name":"Helmholtz Munich, Oberschleißheim, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5989-7922","authenticated-orcid":false,"given":"Melanie","family":"Boxberg","sequence":"additional","affiliation":[{"name":"German Cancer Consortium, Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7881-1749","authenticated-orcid":false,"given":"Tingying","family":"Peng","sequence":"additional","affiliation":[{"name":"Helmholtz Munich, Oberschleißheim, Germany"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-017-0179-5"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trecan.2022.02.007"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-04075-z"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s00428-020-02894-6"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01343-4"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00333-z"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.5858\/arpa.2018-0343-RA"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3031549"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26643-8"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0177-5"},{"key":"ref11","first-page":"99","article-title":"S5CL: Unifying fully-supervised, self-supervised, and semi-supervised learning through hierarchical contrastive learning","volume-title":"Medical Image Computing and Computer Assisted Intervention\u2013(MICCAI)","author":"Tran","year":"2022"},{"key":"ref12","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. 34th Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Khosla"},{"key":"ref13","first-page":"16660","article-title":"Use all the labels: A hierarchical multi-label contrastive learning framework","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"Zhang"},{"key":"ref14","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. 34th Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Sohn"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002730"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0101-9"},{"volume-title":"Radiology data from the cancer genome atlas colon adenocarcinoma [tcga-coad] collection. The cancer imaging archive","year":"2016","author":"Kirk","key":"ref19"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2764"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1607591"},{"key":"ref22","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.00975"},{"key":"ref24","first-page":"12310","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Zbontar"},{"key":"ref25","first-page":"1","article-title":"Do self-supervised and supervised methods learn similar visual representations?","volume-title":"Proc. 2nd Workshop Self-Supervised Learn., Theory Pract. (NeurIPS)","author":"Grigg"},{"key":"ref26","first-page":"22243","article-title":"Big self-supervised models are strong semi-supervised learners","volume-title":"Proc. 34th Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Chen"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00833"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19821-2_26"},{"key":"ref31","first-page":"1298","article-title":"Data2Vec: A general framework for self-supervised learning in speech, vision and language","volume-title":"Proc. 39th Int. Conf. Mach. Learn. (ICML)","author":"Baevski"},{"key":"ref32","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. 9th Int. Conf. Learn. Represent. (ICLR)","author":"Dosovitskiy"},{"key":"ref33","first-page":"1","article-title":"Self-supervised vision transformers learn visual concepts in histopathology","volume-title":"Proc. Learn. Meaningful Represent. Life (NeurIPS)","author":"Chen"},{"key":"ref34","first-page":"16144","article-title":"Scaling vision transformers to gigapixel images via hierarchical self-supervised learning","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"Chen"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102645"},{"key":"ref36","first-page":"1","article-title":"Unsupervised representation learning by predicting image rotations","volume-title":"Proc. 6th Int. Conf. Learn. Represent. (ICLR)","author":"Gidaris"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_5"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102032"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-020-0085-8"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00611"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00156"},{"key":"ref45","first-page":"14421","article-title":"Class-aware contrastive semi-supervised learning","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)","author":"Yang"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104253"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103124"},{"key":"ref48","article-title":"Attention-challenging multiple instance learning for whole slide image classification","author":"Zhang","year":"2023","journal-title":"arXiv:2311.07125"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI52829.2022.9761678"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/176"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00391"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101866"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101996"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101890"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1038\/s41379-021-00807-9"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICFTIC54370.2021.9647390"},{"key":"ref59","article-title":"B-CNN: Branch convolutional neural network for hierarchical classification","author":"Zhu","year":"2017","journal-title":"arXiv:1709.09890"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2022.07.002"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICHI48887.2020.9374332"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2009.5193250"},{"volume":"1","volume-title":"Digestive System Tumours","year":"2019","key":"ref63"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101544"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref66","first-page":"21271","article-title":"Bootstrap your own latent\u2014A new approach to self-supervised learning","volume-title":"Proc. 34th Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Grill"},{"key":"ref67","first-page":"15750","article-title":"Exploring simple Siamese representation learning","volume-title":"Proc. IEEE Comput. Soc. Conf. Comput. Vision Pattern Recognit.","author":"Chen"},{"key":"ref68","article-title":"Self-supervised learning with Swin transformers","author":"Xie","year":"2021","journal-title":"arXiv:2105.04553"},{"key":"ref69","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv:2003.04297"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00901"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00041"},{"key":"ref72","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref74","first-page":"10096","article-title":"EfficientNetv2: Smaller models and faster training","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3056023"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102256"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1111\/his.13975"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/10982228\/10849962.pdf?arnumber=10849962","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T17:56:08Z","timestamp":1746467768000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10849962\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":77,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2025.3532728","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"type":"print","value":"0278-0062"},{"type":"electronic","value":"1558-254X"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}