{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:21:41Z","timestamp":1778538101918,"version":"3.51.4"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049773","type":"print"},{"value":"9783032049780","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04978-0_32","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T16:17:43Z","timestamp":1758212263000},"page":"332-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["HASD: Hierarchical Adaption for\u00a0Pathology Slide-Level Domain-Shift"],"prefix":"10.1007","author":[{"given":"Jingsong","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Deutges","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ario","family":"Sadafi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"You","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katharina","family":"Breininger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nassir","family":"Navab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter J.","family":"Sch\u00fcffler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"32_CR1","unstructured":"Carretero, I., Meseguer, P., del Amor, R., Naranjo, V.: Enhancing whole slide image classification through supervised contrastive domain adaptation. arXiv preprint arXiv:2412.04260 (2024)"},{"issue":"3","key":"32_CR2","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","volume":"30","author":"RJ Chen","year":"2024","unstructured":"Chen, R.J., et al.: Towards a general-purpose foundation model for computational pathology. Nat. Med. 30(3), 850\u2013862 (2024)","journal-title":"Nat. Med."},{"key":"32_CR3","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. Adv. Neural Inf. Process. Syst. 26 (2013)"},{"issue":"6","key":"32_CR4","doi-asserted-by":"publisher","first-page":"2707","DOI":"10.1021\/pr501254j","volume":"14","author":"NJ Edwards","year":"2015","unstructured":"Edwards, N.J., et al.: The cptac data portal: a resource for cancer proteomics research. J. Proteome Res. 14(6), 2707\u20132713 (2015)","journal-title":"J. Proteome Res."},{"key":"32_CR5","unstructured":"Falahkheirkhah, K., Lu, A., Alvarez-Melis, D., Huynh, G.: Domain adaptation using optimal transport for invariant learning using histopathology datasets. arXiv preprint arXiv:2303.02241 (2023)"},{"key":"32_CR6","doi-asserted-by":"publisher","unstructured":"Fan, J., Lv, T., Di, Y., Li, L., Pan, X.: Pathmamba: weakly supervised state space model for multi-class segmentation of pathology images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 500\u2013509. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_47","DOI":"10.1007\/978-3-031-72111-3_47"},{"issue":"1","key":"32_CR7","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41379-021-00911-w","volume":"35","author":"S Farahmand","year":"2022","unstructured":"Farahmand, S., et al.: Deep learning trained on hematoxylin and eosin tumor region of interest predicts her2 status and trastuzumab treatment response in her2+ breast cancer. Mod. Pathol. 35(1), 44\u201351 (2022)","journal-title":"Mod. Pathol."},{"issue":"59","key":"32_CR8","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Ganz, J., et al.: Assessment of scanner domain shifts in deep multiple instance learning. In: BVM Workshop, pp. 137\u2013142. Springer, Heidelberg (2024)","DOI":"10.1007\/978-3-658-44037-4_41"},{"key":"32_CR10","unstructured":"Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning, pp. 2127\u20132136. PMLR (2018)"},{"key":"32_CR11","unstructured":"de\u00a0Jong, E.D., Marcus, E., Teuwen, J.: Current pathology foundation models are unrobust to medical center differences. arXiv preprint arXiv:2501.18055 (2025)"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhu, C., Xu, F., Jia, X., Shi, Z., Jin, M.: Bci: breast cancer immunohistochemical image generation through pyramid pix2pix. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1815\u20131824 (2022)","DOI":"10.1109\/CVPRW56347.2022.00198"},{"key":"32_CR13","doi-asserted-by":"publisher","unstructured":"Naranjo, V.: Domain adaptation for unsupervised cancer detection: an application for skin whole slides images from an interhospital dataset. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72083-3_6","DOI":"10.1007\/978-3-031-72083-3_6"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Peyr\u00e9, G., Cuturi, M., et\u00a0al.: Computational optimal transport: with applications to data science. Found. Trends\u00ae Mach. Learn. 11(5-6), 355\u2013607 (2019)","DOI":"10.1561\/2200000073"},{"key":"32_CR15","doi-asserted-by":"publisher","unstructured":"Pocevi\u010di\u016bt\u0117, M., Eilertsen, G., Garvin, S., Lundstr\u00f6m, C.: Detecting domain shift in multiple instance learning for digital pathology using fr\u00e9chet domain distance. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 157\u2013167. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-43904-9_16","DOI":"10.1007\/978-3-031-43904-9_16"},{"issue":"5","key":"32_CR16","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/38.946629","volume":"21","author":"E Reinhard","year":"2001","unstructured":"Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34\u201341 (2001)","journal-title":"IEEE Comput. Graph. Appl."},{"key":"32_CR17","doi-asserted-by":"publisher","unstructured":"Reisenb\u00fcchler, D., Luttner, L., Schaadt, N.S., Feuerhake, F., Merhof, D.: Unsupervised latent stain adaptation for computational pathology. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 755\u2013765. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72120-5_70","DOI":"10.1007\/978-3-031-72120-5_70"},{"key":"32_CR18","doi-asserted-by":"publisher","unstructured":"Sharma, Y., Syed, S., Brown, D.E.: Mani: maximizing mutual information for nuclei cross-domain unsupervised segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 345\u2013355. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-16434-7_34","DOI":"10.1007\/978-3-031-16434-7_34"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Somasegar, S., et al.: Trends in uterine cancer mortality in the united states: a 50-year population-based analysis. Obstet. Gynecol. 142(4), 978\u2013986 (2023)","DOI":"10.1097\/AOG.0000000000005321"},{"key":"32_CR20","unstructured":"Thorpe, M.: Introduction to optimal transport. Notes of Course at University of Cambridge (2018)"},{"key":"32_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103400","volume":"73","author":"P Wang","year":"2022","unstructured":"Wang, P., Li, P., Li, Y., Xu, J., Jiang, M.: Classification of histopathological whole slide images based on multiple weighted semi-supervised domain adaptation. Biomed. Signal Process. Control 73, 103400 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"32_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-030-87237-3_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Z Wang","year":"2021","unstructured":"Wang, Z., et al.: Instance-aware feature alignment for cross-domain cell nuclei detection in histopathology images. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 499\u2013508. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_48"},{"issue":"10","key":"32_CR23","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","volume":"45","author":"JN Weinstein","year":"2013","unstructured":"Weinstein, J.N., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113\u20131120 (2013)","journal-title":"Nat. Genet."},{"key":"32_CR24","doi-asserted-by":"publisher","unstructured":"Xiong, Y., Liu, J., Zaripova, K., Sharifzadeh, S., Keicher, M., Navab, N.: Prior-radgraphformer: a prior-knowledge-enhanced transformer for generating radiology graphs from x-rays. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 54\u201363. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-55088-1_5","DOI":"10.1007\/978-3-031-55088-1_5"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Xu, Y., Chen, H.: Multimodal optimal transport-based co-attention transformer with global structure consistency for survival prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 21241\u201321251 (2023)","DOI":"10.1109\/ICCV51070.2023.01942"},{"key":"32_CR26","unstructured":"Xu, Z., et al.: Nerf-based cbct reconstruction needs normalization and initialization. arXiv preprint arXiv:2506.19742 (2025)"},{"key":"32_CR27","unstructured":"Yeaton, A., Krishnan, R.G., Mieloszyk, R., Alvarez-Melis, D., Huynh, G.: Hierarchical optimal transport for comparing histopathology datasets. arXiv preprint arXiv:2204.08324 (2022)"},{"key":"32_CR28","doi-asserted-by":"publisher","unstructured":"Yin, C., Liu, S., Wong, V.W.S., Yuen, P.C.: Histosyn: histomorphology-focused pathology image synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 200\u2013210. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-72083-3_19","DOI":"10.1007\/978-3-031-72083-3_19"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04978-0_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:06:34Z","timestamp":1758233194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04978-0_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049773","9783032049780"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04978-0_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}