{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:40:16Z","timestamp":1781250016431,"version":"3.54.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051844","type":"print"},{"value":"9783032051851","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-05185-1_37","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T23:47:09Z","timestamp":1758325629000},"page":"379-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MeDi: Metadata-Guided Diffusion Models for\u00a0Mitigating Biases in\u00a0Tumor Classification"],"prefix":"10.1007","author":[{"given":"David Jacob","family":"Drexlin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Dippel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Hense","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niklas","family":"Preni\u00dfl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gr\u00e9goire","family":"Montavon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederick","family":"Klauschen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus-Robert","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"37_CR1","unstructured":"Alber, M., et\u00a0al.: Atlas: a novel pathology foundation model by mayo clinic, charit\u00e9, and aignostics. arXiv preprint arXiv:2501.05409 (2025)"},{"key":"37_CR2","unstructured":"Aversa, M., et\u00a0al.: Diffinfinite: large mask-image synthesis via parallel random patch diffusion in histopathology. Advances in Neural Information Processing Systems 36 (2024)"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Campanella, G., et al.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Med. 25(8), 1301\u20131309 (2019)","DOI":"10.1038\/s41591-019-0508-1"},{"key":"37_CR4","doi-asserted-by":"crossref","unstructured":"Carrillo-Perez, F., et al.: Rna-to-image multi-cancer synthesis using cascaded diffusion models. bioRxiv, pp. 2023\u201301 (2023)","DOI":"10.1101\/2023.01.13.523899"},{"issue":"3","key":"37_CR5","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."},{"issue":"10","key":"37_CR6","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1038\/s41591-019-0583-3","volume":"25","author":"P Courtiol","year":"2019","unstructured":"Courtiol, P., et al.: Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25(10), 1519\u20131525 (2019)","journal-title":"Nat. Med."},{"key":"37_CR7","doi-asserted-by":"crossref","unstructured":"de Bel, T., Bokhorst, J.M., van der Laak, J., Litjens, G.: Residual cyclegan for robust domain transformation of histopathological tissue slides. Med. Image Anal. 70, 102004 (2021)","DOI":"10.1016\/j.media.2021.102004"},{"key":"37_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2024.114292","volume":"211","author":"G Dernbach","year":"2024","unstructured":"Dernbach, G., et al.: Dissecting ai-based mutation prediction in lung adenocarcinoma: a comprehensive real-world study. Eur. J. Cancer 211, 114292 (2024)","journal-title":"Eur. J. Cancer"},{"key":"37_CR9","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"37_CR10","unstructured":"Dippel, J., et\u00a0al.: Rudolfv: a foundation model by pathologists for pathologists. arXiv preprint arXiv:2401.04079 (2024)"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Dippel, J., et\u00a0al.: Ai-based anomaly detection for clinical-grade histopathological diagnostics. NEJM AI 1(11), AIoa2400468 (2024)","DOI":"10.1056\/AIoa2400468"},{"issue":"11","key":"37_CR12","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R., et al.: Shortcut learning in deep neural networks. Nature Mach. Intell. 2(11), 665\u2013673 (2020)","journal-title":"Nature Mach. Intell."},{"issue":"1","key":"37_CR13","doi-asserted-by":"publisher","first-page":"4423","DOI":"10.1038\/s41467-021-24698-1","volume":"12","author":"FM Howard","year":"2021","unstructured":"Howard, F.M., et al.: The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat. Commun. 12(1), 4423 (2021)","journal-title":"Nat. Commun."},{"key":"37_CR14","unstructured":"Jaume, G., et\u00a0al.: Hest-1k: a dataset for spatial transcriptomics and histology image analysis. arXiv preprint arXiv:2406.16192 (2024)"},{"key":"37_CR15","unstructured":"de\u00a0Jong, E.D., Marcus, E., Teuwen, J.: Current pathology foundation models are unrobust to medical center differences (2025). https:\/\/arxiv.org\/abs\/2501.18055"},{"issue":"3","key":"37_CR16","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1038\/s42256-025-01000-2","volume":"7","author":"J Kauffmann","year":"2025","unstructured":"Kauffmann, J., Dippel, J., Ruff, L., Samek, W., M\u00fcller, K.R., Montavon, G.: Explainable ai reveals clever hans effects in unsupervised learning models. Nature Mach. Intell. 7(3), 412\u2013422 (2025)","journal-title":"Nature Mach. Intell."},{"key":"37_CR17","doi-asserted-by":"crossref","unstructured":"Klauschen, F., et al.: Toward explainable artificial intelligence for precision pathology. Ann. Rev. Pathol. Mech. Disease 19(Volume 19, 2024), 541\u2013570 (2024). https:\/\/www.annualreviews.org\/content\/journals\/10.1146\/annurev-pathmechdis-051222-113147","DOI":"10.1146\/annurev-pathmechdis-051222-113147"},{"key":"37_CR18","unstructured":"K\u00f6men, J., Marienwald, H., Dippel, J., Hense, J.: Do histopathological foundation models eliminate batch effects? a comparative study. arXiv preprint arXiv:2411.05489 (2024)"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Komura, D., et al.: Universal Encoding of Pan-cancer Histology by Deep Texture Representations. Cell Rep. 38(9), 110424 (2022)","DOI":"10.1016\/j.celrep.2022.110424"},{"key":"37_CR20","unstructured":"Ktena, I., et\u00a0al.: Generative models improve fairness of medical classifiers under distribution shifts. Nature Med., 1\u20138 (2024)"},{"key":"37_CR21","doi-asserted-by":"crossref","unstructured":"Lapuschkin, S., W\u00e4ldchen, S., Binder, A., Montavon, G., Samek, W., M\u00fcller, K.R.: Unmasking clever hans predictors and assessing what machines really Learn. Nat. Commun. 10(1), 1096 (2019)","DOI":"10.1038\/s41467-019-08987-4"},{"key":"37_CR22","doi-asserted-by":"crossref","unstructured":"Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: from Nano to Macro, pp. 1107\u20131110. IEEE (2009)","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"37_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108410","volume":"175","author":"JM Niehues","year":"2024","unstructured":"Niehues, J.M., et al.: Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance. Comput. Biol. Med. 175, 108410 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"13","key":"37_CR24","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.3390\/diagnostics14131442","volume":"14","author":"P Osorio","year":"2024","unstructured":"Osorio, P., et al.: Latent diffusion models with image-derived annotations for enhanced ai-assisted cancer diagnosis in histopathology. Diagnostics 14(13), 1442 (2024)","journal-title":"Diagnostics"},{"issue":"5","key":"37_CR25","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. Graphics Appl. 21(5), 34\u201341 (2001)","journal-title":"IEEE Comput. Graphics Appl."},{"issue":"4","key":"37_CR26","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1038\/s41591-024-02885-z","volume":"30","author":"A Vaidya","year":"2024","unstructured":"Vaidya, A., et al.: Demographic bias in misdiagnosis by computational pathology models. Nat. Med. 30(4), 1174\u20131190 (2024)","journal-title":"Nat. Med."},{"key":"37_CR27","unstructured":"Vorontsov, E., et\u00a0al.: A foundation model for clinical-grade computational pathology and rare cancers detection. Nature medicine, pp. 1\u201312 (2024)"}],"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\/978-3-032-05185-1_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T23:47:16Z","timestamp":1758325636000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05185-1_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051844","9783032051851"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05185-1_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"F.K. and K.R.M. are cofounders and J.D. is an employee of the Aignostics GmbH, which develops AI algorithms for pathology. The other 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"}}]}}