{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T13:47:00Z","timestamp":1790948820051,"version":"4.1.0"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2021R1A2C2008576"],"award-info":[{"award-number":["2021R1A2C2008576"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Institute of Information & Communications Technology Planning & Evaluation (IITP)\u2014Innovative Human Resource Development for Local Intellectualization program","award":["IITP-2024-RS-2024-00436773"],"award-info":[{"award-number":["IITP-2024-RS-2024-00436773"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Prostate cancer is diagnosed through a comprehensive study of histopathology slides, which takes time and requires professional interpretation. To minimize this load, we developed a semi-supervised learning technique that combines transformer-based representation learning and a custom TransUNet classifier. To capture a wide range of morphological structures without manual annotation, our method pretrains DINOv2 on 10,000 unlabeled prostate tissue patches. After receiving the transformer-derived features, a bespoke CNN-based decoder uses residual upsampling and carefully constructed skip connections to merge data from many spatial scales. Expert pathologists identified only 20% of the patches in the whole dataset; the remaining unlabeled samples were contributed by using a consistency-driven learning method that promoted reliable predictions across various augmentations. The model received precision and recall scores of 91.81% and 89.02%, respectively, and an accuracy of 93.78% on an additional test set. These results exceed the performance of a conventional U-Net and a baseline encoder\u2013decoder network. All things considered, the localized CNN (Convolutional Neural Network) decoding and global transformer attention provide a reliable method for prostate cancer classification in situations with little annotated data.<\/jats:p>","DOI":"10.3390\/make8020026","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T13:55:34Z","timestamp":1769176534000},"page":"26","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Transformer-Driven Semi-Supervised Learning for Prostate Cancer Histopathology: A DINOv2\u2013TransUNet Framework"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3309-6563","authenticated-orcid":false,"given":"Rubina Akter","family":"Rabeya","sequence":"first","affiliation":[{"name":"Department of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0242-1805","authenticated-orcid":false,"given":"Jeong-Wook","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Pathology, Incheon Sejong Hospital, Seoul 21080, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nam Hoon","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Pathology, Severance Hospital, Yonsei University, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5399-7647","authenticated-orcid":false,"given":"Hee-Cheol","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1372-7052","authenticated-orcid":false,"given":"Heung-Kook","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Inje University, Gimhae 50834, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sekhoacha, M., Riet, K., Motloung, P., Gumenku, L., Adegoke, A., and Mashele, S. 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