{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:25:50Z","timestamp":1779261950850,"version":"3.51.4"},"reference-count":72,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Brain Pool Program through the National Research Foundation of Korea"},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["RS-2023-00283791"],"award-info":[{"award-number":["RS-2023-00283791"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010002","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2021R1I1A3056903"],"award-info":[{"award-number":["2021R1I1A3056903"]}],"id":[{"id":"10.13039\/100010002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3570673","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T17:33:41Z","timestamp":1747330421000},"page":"86356-86379","source":"Crossref","is-referenced-by-count":5,"title":["Prototypical Few-Shot Learning for Histopathology Classification: Leveraging Foundation Models With Adapter Architectures"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9967-1538","authenticated-orcid":false,"given":"Kazi Rakib","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Biomedical Science, Kyungpook National University, Daegu, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Science, Kyungpook National University, Daegu, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9715-1440","authenticated-orcid":false,"given":"Junghwan","family":"Cho","sequence":"additional","affiliation":[{"name":"Clinical Omics Institution, Kyungpook National University, Daegu, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7200-0921","authenticated-orcid":false,"given":"Hyung Soo","family":"Han","sequence":"additional","affiliation":[{"name":"Clinical Omics Institution, Kyungpook National University, Daegu, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2011.02.006"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/path.6163"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.4103\/2153-3539.186902"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101813"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-05881-4"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02271"},{"key":"ref7","article-title":"RudolfV: A foundation model by pathologists for pathologists","author":"Dippel","year":"2024","journal-title":"arXiv:2401.04079"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref9","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Caron"},{"key":"ref10","first-page":"12310","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"\u017dbontar"},{"key":"ref11","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020","journal-title":"arXiv:2003.04297"},{"key":"ref12","article-title":"Scaling self-supervised learning for histopathology with masked image modeling","author":"Filiot","year":"2023","journal-title":"medRxiv"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/isbi.2019.8759182"},{"key":"ref14","first-page":"1","article-title":"Siamese neural networks for one-shot image recognition","volume-title":"Proc. ICML Deep Learn. Workshop","author":"Koch"},{"key":"ref15","first-page":"3637","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Vinyals"},{"key":"ref16","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3"},{"key":"ref18","article-title":"Benchmarking pathology foundation models: Adaptation strategies and scenarios","author":"Lee","year":"2024","journal-title":"arXiv:2410.16038"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00326"},{"key":"ref20","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Houlsby"},{"key":"ref21","article-title":"LoRA: Low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv:2106.09685"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100198"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103289"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559"},{"key":"ref27","article-title":"Less is more: Selective layer finetuning with SubTuning","author":"Kaplun","year":"2023","journal-title":"arXiv:2302.06354"},{"key":"ref28","article-title":"Surgical fine-tuning improves adaptation to distribution shifts","author":"Lee","year":"2022","journal-title":"arXiv:2210.11466"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01891-x"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00251"},{"key":"ref31","article-title":"Towards few-shot adaptation of foundation models via multitask finetuning","author":"Xu","year":"2024","journal-title":"arXiv:2402.15017"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00075"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102406"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI58124.2022.00048"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/cai59869.2024.00177"},{"key":"ref36","article-title":"Few-shot adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Motiian"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref38","article-title":"FHIST: A benchmark for few-shot classification of histological images","author":"Shakeri","year":"2022","journal-title":"arXiv:2206.00092"},{"key":"ref39","article-title":"Cross-domain evaluation of few-shot classification models: Natural images vs. histopathological images","author":"Sekhar","year":"2024","journal-title":"arXiv:2410.09176"},{"key":"ref40","article-title":"SimpleShot: Revisiting nearest-neighbor classification for few-shot learning","author":"Wang","year":"2019","journal-title":"arXiv:1911.04623"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_43"},{"key":"ref42","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10801660"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-45528-0"},{"key":"ref46","volume":"75","author":"Cheng","year":"2020","journal-title":"Function Theory Lp Spaces"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCS45141.2019.9065747"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511809071"},{"key":"ref49","first-page":"24581","article-title":"On episodes, prototypical networks, and few-shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Laenen"},{"key":"ref50","volume-title":"100,000 histological images of human colorectal cancer and healthy tissue","author":"Kather","year":"2018"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1038\/srep27988"},{"key":"ref52","article-title":"Lung and colon cancer histopathological image dataset (LC25000)","author":"Borkowski","year":"2019","journal-title":"arXiv:1912.12142"},{"key":"ref53","first-page":"5637","article-title":"WILDS: A benchmark of in-the-wild distribution shifts","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Koh"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_24"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giy065"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2867350"},{"key":"ref57","first-page":"11525","article-title":"Dash: Semi-supervised learning with dynamic thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref58","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sohn"},{"key":"ref59","first-page":"18408","article-title":"FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"ref62","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017","journal-title":"arXiv:1711.05101"},{"key":"ref63","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016","journal-title":"arXiv:1608.03983"},{"key":"ref64","article-title":"Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images","author":"Pucci","year":"2024","journal-title":"arXiv:2404.03474"},{"issue":"86","key":"ref65","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01166-w"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2011.122"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-020-00169-2"},{"key":"ref70","article-title":"PRISM: A multi-modal generative foundation model for slide-level histopathology","author":"Shaikovski","year":"2024","journal-title":"arXiv:2405.10254"},{"key":"ref71","volume-title":"H-Optimus-0","author":"Saillard","year":"2024"},{"key":"ref72","first-page":"169","article-title":"Addressing data scarcity in histopathology: A few-shot learning approach with adapter-enhanced pre-trained foundation models","volume-title":"Proc. ASDP Annu. Meeting","author":"Kazi"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11005525.pdf?arnumber=11005525","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T18:04:33Z","timestamp":1748282673000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11005525\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":72,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3570673","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}