{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:47:27Z","timestamp":1771958847471,"version":"3.50.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271115"],"award-info":[{"award-number":["62271115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1109\/tmi.2025.3595111","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T18:45:22Z","timestamp":1754333122000},"page":"4023-4036","source":"Crossref","is-referenced-by-count":2,"title":["VLM-CPL: Consensus Pseudo-Labels From Vision-Language Models for Annotation-Free Pathological Image Classification"],"prefix":"10.1109","volume":"44","author":[{"given":"Lanfeng","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Zongyao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Affiliated Cancer Hospital, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Affiliated Cancer Hospital, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Wenjun","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Shichuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-158X","authenticated-orcid":false,"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8719-448X","authenticated-orcid":false,"given":"Shaoting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)32998-8"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0508-1"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/srep27988"},{"key":"ref4","first-page":"2127","article-title":"Attention-based deep multiple instance learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ilse"},{"key":"ref5","first-page":"2136","article-title":"TransMIL: Transformer based correlated multiple instance learning for whole slide image classification","volume-title":"Proc. NeurIPS","author":"Shao"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102517"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00976"},{"key":"ref9","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02504-3"},{"key":"ref11","article-title":"BiomedCLIP: A multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","author":"Zhang","year":"2023","journal-title":"arXiv:2303.00915"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02856-4"},{"key":"ref13","first-page":"60984","article-title":"Enhancing CLIP with CLIP: Exploring pseudolabeling for limited-label prompt tuning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Menghini"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16440-8_55"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3161787"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102640"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101816"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-020-00682-w"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72083-3_28"},{"key":"ref21","first-page":"4904","article-title":"Scaling up visual and vision-language representation learning with noisy text supervision","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Jia"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01893"},{"key":"ref23","first-page":"37995","article-title":"Quilt-1M: One million image-text pairs for histopathology","volume-title":"Proc. NeurIPS","author":"Ikezogwo"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.4135\/9781071810118"},{"key":"ref25","first-page":"7164","article-title":"How does disagreement help generalization against label corruption","volume-title":"Proc. ICML","author":"Yu"},{"key":"ref26","first-page":"1","article-title":"DivideMix: Learning with noisy labels as semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3269798"},{"key":"ref28","first-page":"8778","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. NIPS","author":"Zhang"},{"key":"ref29","first-page":"20331","article-title":"Earlylearning regularization prevents memorization of noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","volume":"33","author":"Liu"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.008"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.103"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17027-0_5"},{"key":"ref33","first-page":"1","article-title":"Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks","volume-title":"Proc. Med. Imag. Deep Learn.","author":"Ayhan"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530"},{"key":"ref35","first-page":"1027","article-title":"K-means++ the advantages of careful seeding","volume-title":"Proc. 18th Annu. ACM-SIAM Symp. Discrete Algorithms","author":"Arthur"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2011.2170414"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43895-0_1"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102076"},{"key":"ref39","article-title":"Lung and colon cancer histopathological image dataset (LC25000)","author":"Borkowski","year":"2019","journal-title":"arXiv:1912.12142"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102485"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.3389\/fradi.2024.1460889"},{"key":"ref42","first-page":"1","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dosovitskiy"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3"},{"key":"ref44","first-page":"1","article-title":"LoRA: Low-rank adaptation of large language models","volume-title":"Proc. Int. Conf. Learn. Represen.","author":"Hu"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01836"},{"key":"ref46","article-title":"Does CLIP benefit visual question answering in the medical domain as much as it does in the general domain?","author":"Eslami","year":"2021","journal-title":"arXiv:2112.13906"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3384961"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.00949"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3313509"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/11218268\/11108704.pdf?arnumber=11108704","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T17:35:33Z","timestamp":1761759333000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11108704\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":50,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2025.3595111","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]}}}