{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:42:49Z","timestamp":1758267769316,"version":"3.44.0"},"publisher-location":"Cham","reference-count":30,"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_2","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T16:16:25Z","timestamp":1758212185000},"page":"13-23","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for\u00a0Chest X-Rays"],"prefix":"10.1007","author":[{"given":"Chenlang","family":"Yi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zizhan","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiyuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Girish","family":"Bathla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching-Long","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bobak J.","family":"Mortazavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianbao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"2_CR1","unstructured":"Agarwal, A., Beygelzimer, A., Dud\u00edk, M., Langford, J., Wallach, H.: A reductions approach to fair classification. In: International Conference on Machine Learning, pp. 60\u201369. PMLR (2018)"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., et al.: Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323 (2019)","DOI":"10.18653\/v1\/W19-1909"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Berg, H., et al.: A prompt array keeps the bias away: debiasing vision-language models with adversarial learning. arXiv:2203.11933 (2022)","DOI":"10.18653\/v1\/2022.aacl-main.61"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Beutel, A., et al.: Fairness in recommendation ranking through pairwise comparisons. arXiv:1903.00780 (2019)","DOI":"10.1145\/3292500.3330745"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Brown, A., Tomasev, N., Freyberg, J., Liu, Y., Karthikesalingam, A., Schrouff, J.: Detecting and preventing shortcut learning for fair medical AI using shortcut testing (short). arXiv preprint arXiv:2207.10384 (2022)","DOI":"10.1038\/s41467-023-39902-7"},{"key":"2_CR6","unstructured":"Chambon, P., et al.: Chexpert plus: augmenting a large chest x-ray dataset with text radiology reports, patient demographics and additional image formats. arXiv:2405.19538 (2024)"},{"key":"2_CR7","unstructured":"Diederik, P.K.: Adam: a method for stochastic optimization. (No Title) (2014)"},{"issue":"59","key":"2_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":"2_CR9","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Acharya, A., Jain, R., Saha, S., Chadha, A., Sinha, S.: Clipsyntel: clip and LLM synergy for multimodal question summarization in healthcare. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 22031\u201322039 (2024)","DOI":"10.1609\/aaai.v38i20.30206"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Glocker, B., Jones, C., Bernhardt, M., Winzeck, S.: Algorithmic encoding of protected characteristics in chest x-ray disease detection models. EBioMedicine 89 (2023)","DOI":"10.1016\/j.ebiom.2023.104467"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Huang, S.C., Shen, L., Lungren, M.P., Yeung, S.: Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3942\u20133951 (2021)","DOI":"10.1109\/ICCV48922.2021.00391"},{"issue":"9","key":"2_CR13","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1038\/s41591-023-02504-3","volume":"29","author":"Z Huang","year":"2023","unstructured":"Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T.J., Zou, J.: A visual-language foundation model for pathology image analysis using medical twitter. Nat. Med. 29(9), 2307\u20132316 (2023)","journal-title":"Nat. Med."},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Irvin, J., et\u00a0al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 590\u2013597 (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"2_CR15","unstructured":"Jin, R., et al.: FairMedFM: fairness benchmarking for medical imaging foundation models. In: Advances in Neural Information Processing Systems, vol. 37, pp. 111318\u2013111357 (2024)"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Scientific Data 6(1), 317 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"2_CR17","unstructured":"Khan, M.O., Afzal, M.M., Mirza, S., Fang, Y.: How fair are medical imaging foundation models? In: Machine Learning for Health (ML4H), pp. 217\u2013231. PMLR (2023)"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Luo, Y., et\u00a0al.: FairCLIP: harnessing fairness in vision-language learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12289\u201312301 (2024)","DOI":"10.1109\/CVPR52733.2024.01168"},{"issue":"6464","key":"2_CR19","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464), 447\u2013453 (2019)","journal-title":"Science"},{"key":"2_CR20","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"2_CR21","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I.Y., Ghassemi, M.: CheXclusion: fairness gaps in deep chest X-ray classifiers. In: BIOCOMPUTING 2021: Proceedings of the Pacific Symposium, pp. 232\u2013243. World Scientific (2020)","DOI":"10.1142\/9789811232701_0022"},{"issue":"12","key":"2_CR23","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M.B., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27(12), 2176\u20132182 (2021)","journal-title":"Nat. Med."},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, Y., Wang, X.E.: Are gender-neutral queries really gender-neutral? Mitigating gender bias in image search (2021). https:\/\/arxiv.org\/abs\/2109.05433","DOI":"10.18653\/v1\/2021.emnlp-main.151"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Agarwal, D., Sun, J.: Medclip: contrastive learning from unpaired medical images and text. arXiv preprint arXiv:2210.10163 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"2_CR26","unstructured":"Yao, Y., Lin, Q., Yang, T.: Stochastic methods for AUC optimization subject to AUC-based fairness constraints. In: International Conference on Artificial Intelligence and Statistics, pp. 10324\u201310342. PMLR (2023)"},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"You, K., et al.: CXR-CLIP: toward large scale chest X-ray language-image pre-training. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 101\u2013111. Springer (2023)","DOI":"10.1007\/978-3-031-43895-0_10"},{"key":"2_CR28","unstructured":"Zhang, H., Dullerud, N., Roth, K., Oakden-Rayner, L., Pfohl, S., Ghassemi, M.: Improving the fairness of chest x-ray classifiers. In: Conference on Health, Inference, and Learning, pp. 204\u2013233. PMLR (2022)"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, S., et al.: Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs (2025). https:\/\/arxiv.org\/abs\/2303.00915","DOI":"10.1056\/AIoa2400640"},{"key":"2_CR30","unstructured":"Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P.: Contrastive learning of medical visual representations from paired images and text. In: Machine Learning for Healthcare Conference, pp. 2\u201325. PMLR (2022)"}],"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_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:05:03Z","timestamp":1758233103000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04978-0_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049773","9783032049780"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04978-0_2","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"}}]}}