{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T02:14:12Z","timestamp":1783304052584,"version":"3.54.6"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T00:00:00Z","timestamp":1777939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"vor","delay-in-days":62,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal University Jaipur"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Manual detection of breast cancer in histopathology images is a highly complex task due to variations in tissue appearance and the requirement for analysis across multiple magnifications levels. The often overlooked problem in most existing approaches is the number of models required for different magnification levels. Most works in this field use separate models for each magnification, which can limit scalability and practical real-world deployment. This proposed study leverages Transfer Learning and Knowledge Distillation with transformers to develop a unified model capable of handling histopathology images across multiple magnifications. The model is trained sequentially across magnification levels, enabling the transfer and refinement of learned representations. The proposed model effectively generalizes across magnifications by prioritizing common features, achieving an average accuracy of 95.43%, average precision of 94.45%, average recall of 99.20%, average F1 score of 96.76% and AUC of 0.9930 across all magnifications of the BreakHis dataset.<\/jats:p>","DOI":"10.1038\/s41598-026-49792-6","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T21:33:41Z","timestamp":1778016821000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["KDTViT knowledge distillation, transfer learning and transformer based deep learning framework for efficient histopathology image classification"],"prefix":"10.1038","volume":"16","author":[{"given":"Aarv","family":"Mankodi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Praveen Kumar","family":"Shukla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hitesh","family":"Tekchandani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"issue":"12","key":"49792_CR1","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1177\/000313481007601226","volume":"76","author":"J Sariego","year":"2010","unstructured":"Sariego, J. Breast cancer in the young patient. Am. Surg. 76(12), 1397\u20131400 (2010).","journal-title":"Am. Surg."},{"key":"49792_CR2","unstructured":"Federal University of Parana. Breast cancer histopathological database (breakhis). https:\/\/web.inf.ufpr.br\/vri\/databases\/breast-cancer-histopathological-database-breakhis\/ (2016)."},{"issue":"24","key":"49792_CR3","doi-asserted-by":"publisher","first-page":"17778","DOI":"10.1109\/JIOT.2021.3119520","volume":"8","author":"A Kumar","year":"2021","unstructured":"Kumar, A. et al. MobiHisNet: A lightweight CNN in mobile edge computing for histopathological image classification. IEEE Internet Things J. 8(24), 17778\u201317789. https:\/\/doi.org\/10.1109\/JIOT.2021.3119520 (2021).","journal-title":"IEEE Internet Things J."},{"issue":"10","key":"49792_CR4","doi-asserted-by":"publisher","first-page":"5025","DOI":"10.1109\/JBHI.2022.3187765","volume":"26","author":"M Liu","year":"2022","unstructured":"Liu, M. et al. A deep learning method for breast cancer classification in the pathology images. IEEE J. Biomed. Health Inform. 26(10), 5025\u20135032. https:\/\/doi.org\/10.1109\/JBHI.2022.3187765 (2022).","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"49792_CR5","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1109\/TCBB.2022.3163277","volume":"20","author":"M Saini","year":"2023","unstructured":"Saini, M. & Susan, S. Vggin-net: Deep transfer network for imbalanced breast cancer dataset. IEEE ACM Trans. Comput. Biol. Bioinform. 20(1), 752\u2013762. https:\/\/doi.org\/10.1109\/TCBB.2022.3163277 (2023).","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinform."},{"issue":"6","key":"49792_CR6","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1109\/JBHI.2023.3256974","volume":"28","author":"BLY Agbley","year":"2024","unstructured":"Agbley, B. L. Y. et al. Federated fusion of magnified histopathological images for breast tumor classification in the internet of medical things. IEEE J. Biomed. Health Inform. 28(6), 3389\u20133400. https:\/\/doi.org\/10.1109\/JBHI.2023.3256974 (2024).","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"49792_CR7","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1109\/TFUZZ.2024.3400861","volume":"33","author":"Q Li","year":"2025","unstructured":"Li, Q. et al. Fuzzy-vit: A deep neuro-fuzzy system for cross-domain transfer learning from large-scale general data to medical image. IEEE Trans. Fuzzy Syst. 33(1), 231\u2013241. https:\/\/doi.org\/10.1109\/TFUZZ.2024.3400861 (2025).","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"49792_CR8","doi-asserted-by":"publisher","first-page":"117921","DOI":"10.1109\/ACCESS.2024.3445954","volume":"12","author":"S Hao","year":"2024","unstructured":"Hao, S. et al. St-double-net: A two-stage breast tumor classification model based on Swin Transformer and weakly supervised target localization. IEEE Access 12, 117921\u2013117933. https:\/\/doi.org\/10.1109\/ACCESS.2024.3445954 (2024).","journal-title":"IEEE Access"},{"key":"49792_CR9","doi-asserted-by":"publisher","first-page":"35977","DOI":"10.1109\/ACCESS.2022.3163822","volume":"10","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Zhang, C. & Gao, S. Breast cancer classification from histopathological images using resolution adaptive network. IEEE Access 10, 35977\u201335991. https:\/\/doi.org\/10.1109\/ACCESS.2022.3163822 (2022).","journal-title":"IEEE Access"},{"key":"49792_CR10","doi-asserted-by":"publisher","first-page":"25359","DOI":"10.1109\/ACCESS.2024.3366185","volume":"12","author":"J Si","year":"2024","unstructured":"Si, J., Jia, W. & Jiang, H. Breast cancer histopathology images classification through multi-view augmented contrastive learning and pre-learning knowledge distillation. IEEE Access 12, 25359\u201325371. https:\/\/doi.org\/10.1109\/ACCESS.2024.3366185 (2024).","journal-title":"IEEE Access"},{"issue":"2","key":"49792_CR11","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/TCBB.2022.3174091","volume":"20","author":"S Garg","year":"2023","unstructured":"Garg, S. & Singh, P. Transfer learning based lightweight ensemble model for imbalanced breast cancer classification. IEEE ACM Trans. Comput. Biol. Bioinform. 20(2), 1529\u20131539. https:\/\/doi.org\/10.1109\/TCBB.2022.3174091 (2023).","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinform."},{"issue":"1","key":"49792_CR12","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/JBHI.2023.3283042","volume":"28","author":"L Liu","year":"2024","unstructured":"Liu, L. et al. Collaborative transfer network for multi-classification of breast cancer histopathological images. IEEE J. Biomed. Health Inform. 28(1), 110\u2013121. https:\/\/doi.org\/10.1109\/JBHI.2023.3283042 (2024).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"49792_CR13","unstructured":"Mehta, S. & Rastegari, M. MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer. https:\/\/arxiv.org\/abs\/2110.02178 (2022)."},{"key":"49792_CR14","unstructured":"Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"49792_CR15","unstructured":"ICIAR 2018 Grand Challenge. Bach dataset repository. https:\/\/iciar2018-challenge.grand-challenge.org\/Dataset\/ (2018)."},{"key":"49792_CR16","unstructured":"Google Research. Google Colaboratory. https:\/\/colab.research.google.com\/ (2024)."},{"issue":"2","key":"49792_CR17","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","unstructured":"Selvaraju, R. R. et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128(2), 336\u2013359. https:\/\/doi.org\/10.1007\/s11263-019-01228-7 (2019).","journal-title":"Int. J. Comput. Vis."},{"key":"49792_CR18","doi-asserted-by":"publisher","first-page":"68697","DOI":"10.1109\/ACCESS.2024.3397667","volume":"12","author":"V Sreelekshmi","year":"2024","unstructured":"Sreelekshmi, V., Pavithran, K. & Nair, J. J. Swincnn: An integrated Swin Transformer and CNN for improved breast cancer grade classification. IEEE Access 12, 68697\u201368710. https:\/\/doi.org\/10.1109\/ACCESS.2024.3397667 (2024).","journal-title":"IEEE Access"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49792-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49792-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49792-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T01:16:31Z","timestamp":1783300591000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-49792-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,5]]},"references-count":18,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["49792"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-49792-6","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,5]]},"assertion":[{"value":"10 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"All authors have reviewed the final manuscript and consent to its publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"20721"}}