{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:47:12Z","timestamp":1779382032009,"version":"3.53.1"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049803","type":"print"},{"value":"9783032049810","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\/978-3-032-04981-0_12","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:11:11Z","timestamp":1758258671000},"page":"119-129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CXR-CML: Improved Zero-Shot Classification of\u00a0Long-Tailed Multi-label Diseases in\u00a0Chest X-Rays"],"prefix":"10.1007","author":[{"given":"Rajesh","family":"Madhipati","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheethal","family":"Bhat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Buess","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"12_CR1","unstructured":"Bannur, S., et\u00a0al.: Maira-2: grounded radiology report generation. arXiv preprint arXiv:2406.04449 (2024)"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Bhat, S., Panambur, A.B., Mansoor, A., Georgescu, B., Grbic, S., Maier, A.: Towards robust zero-shot chest x-ray classification: exploring data distribution bias in chest x-ray datasets. BVM (2025)","DOI":"10.1007\/978-3-658-47422-5_42"},{"key":"12_CR3","unstructured":"Boecking, B., et\u00a0al.: Cxr-bert: Pretraining chest x-ray reports for multimodal alignment. J. Biomed. Inf. (2022). https:\/\/huggingface.co\/microsoft\/BiomedVLP-CXR-BERT-general"},{"key":"12_CR4","unstructured":"Chen, K., Lei, W., Zhang, R., Zhao, S., Zheng, W., Wang, R.: Pcct: progressive class-center triplet loss for imbalanced medical image classification (2022). https:\/\/arxiv.org\/abs\/2207.04793"},{"key":"12_CR5","doi-asserted-by":"crossref","unstructured":"Delitzas, A., Parelli, M., Hars, N., et\u00a0al., G.V.: Multi-clip: contrastive vision-language pre-training for question answering tasks in 3d scenes. In: 34th British Machine Vision Conference 2023, BMVC 2023, Aberdeen, UK, 20\u201324 November 2023. BMVA (2023). https:\/\/papers.bmvc2023.org\/0748.pdf","DOI":"10.1109\/CVPRW59228.2023.00593"},{"key":"12_CR6","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T.: An image is worth 16x16 words: transformers for image recognition at scale (2021). https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"12_CR7","unstructured":"Du, Y., Chang, B., Dvornek, N.C.: CLEFT. In: Proceedings of Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024. LNCS, vol. 15012. Springer, Cham (2024)"},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Tiu, E., Talius, E., Patel, P., et\u00a0al.: Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning. Nat. Biomed. Eng. 1399\u20131406 (2022)","DOI":"10.1038\/s41551-022-00936-9"},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"Sreena, V.G., Ponraj, N., Deepa, P.L.: Study on public chest x-ray data sets for lung disease classification. In: 2021 3rd International Conference on Signal Processing and Communication (ICPSC), pp. 54\u201358 (2021). https:\/\/doi.org\/10.1109\/ICSPC51351.2021.9451726","DOI":"10.1109\/ICSPC51351.2021.9451726"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Holste, G., et al.: Long-tailed classification of thorax diseases on chest X-ray: a new benchmark study, pp. 22\u201332. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17027-0_3","DOI":"10.1007\/978-3-031-17027-0_3"},{"key":"12_CR11","unstructured":"Huang, Z., et\u00a0al.: Gloria. In: Medical Image Analysis (2021). https:\/\/arxiv.org\/html\/2312.07353v3"},{"key":"12_CR12","unstructured":"Johnson, A.E.W., et al.: Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs (2019). https:\/\/arxiv.org\/abs\/1901.07042"},{"key":"12_CR13","doi-asserted-by":"publisher","unstructured":"Lee, H.Y., Park, H.J., Kim, H.M.: A clarification of the cauchy distribution. Commun. Stat. Appl. Methods 21 (2014). https:\/\/doi.org\/10.5351\/CSAM.2014.21.2.183","DOI":"10.5351\/CSAM.2014.21.2.183"},{"key":"12_CR14","unstructured":"Ley, C., Neven, A.: The value at the mode in multivariate $$t$$ distributions: a curiosity or not? (2014). https:\/\/arxiv.org\/abs\/1211.1174"},{"issue":"3","key":"12_CR15","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1007\/s00181-018-1570-0","volume":"58","author":"R Li","year":"2018","unstructured":"Li, R., Nadarajah, S.: A review of Student\u2019s t distribution and its generalizations. Empir. Econ. 58(3), 1461\u20131490 (2018). https:\/\/doi.org\/10.1007\/s00181-018-1570-0","journal-title":"Empir. Econ."},{"key":"12_CR16","unstructured":"Li, Y., et\u00a0al.: Clip for cxr: fine-tuning clip for chest x-ray disease classification. IEEE TMI (2023)"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Messina, P., Vidal, R., Parra, D., \u00c1lvaro Soto, A.V.: Extracting and encoding: leveraging large language models and medical knowledge to enhance radiological text representation (2024). https:\/\/arxiv.org\/abs\/2407.01948","DOI":"10.18653\/v1\/2024.findings-acl.236"},{"key":"12_CR18","unstructured":"Mu, N., Kirillov, A., Wagner, D., Xie, S.: Slip: self-supervision meets language-image pre-training (2021). https:\/\/arxiv.org\/abs\/2112.12750"},{"key":"12_CR19","unstructured":"Mukherjee, K., Khare, A., Verma, A.: A simple dynamic learning rate tuning algorithm for automated training of dnns (2019). https:\/\/arxiv.org\/pdf\/1910.11605"},{"key":"12_CR20","doi-asserted-by":"publisher","unstructured":"Park, S., Kim, G., Oh, Y.E.A.: Self-evolving vision transformer for chest x-ray diagnosis through knowledge distillation. Nat. Commun. 13, 3848 (2022). https:\/\/doi.org\/10.1038\/s41467-022-31514-x","DOI":"10.1038\/s41467-022-31514-x"},{"issue":"4","key":"12_CR21","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1023\/A:1008981510081","volume":"10","author":"D Peel","year":"2000","unstructured":"Peel, D., McLachlan, G.J.: Robust mixture modelling using the t distribution. Stat. Comput. 10(4), 339\u2013348 (2000)","journal-title":"Stat. Comput."},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Peng, Y., Lin, M., Holste, G., Wang, S., Zhou, E.A.: Cxr-lt 2024: long-tailed, multi-label, and zero- shot classification on chest x-rays (2024). https:\/\/doi.org\/10.5281\/zenodo.10991413","DOI":"10.5281\/zenodo.10991413"},{"key":"12_CR23","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A.: Learning transferable visual models from natural language supervision (2021). https:\/\/arxiv.org\/abs\/2103.00020"},{"key":"12_CR24","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., et\u00a0al., G.G.: Learning transferable visual models from natural language supervision (2021). https:\/\/arxiv.org\/abs\/2103.00020"},{"key":"12_CR25","unstructured":"Seputis, D., Mihailov, S., Chatterjee, S., Xiao, Z.: Multi-modal adapter for vision-language models (2024). https:\/\/arxiv.org\/abs\/2409.02958"},{"key":"12_CR26","doi-asserted-by":"publisher","unstructured":"Shentu, J., Al\u00a0Moubayed, N.: Cxr-irgen: an integrated vision and language model for the generation of clinically accurate chest x-ray image-report pairs. In: WACV, pp. 5200\u20135209 (2024). https:\/\/doi.org\/10.1109\/WACV57701.2024.00513","DOI":"10.1109\/WACV57701.2024.00513"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Speets, A.M., van\u00a0der Graaf, Y., Hoes, A.W., et\u00a0al.: Chest radiography in general practice: indications, diagnostic yield and consequences for patient management (2006). https:\/\/pubmed.ncbi.nlm.nih.gov\/16882374\/","DOI":"10.1183\/09031936.06.00008306"},{"key":"12_CR28","doi-asserted-by":"publisher","unstructured":"Wan, H., Wang, H., Scotney, B., Liu, J.: A novel gaussian mixture model for classification. In: SMC, pp. 3298\u20133303 (2019). https:\/\/doi.org\/10.1109\/SMC.2019.8914215","DOI":"10.1109\/SMC.2019.8914215"},{"key":"12_CR29","unstructured":"Wang, X., et\u00a0al.: Clip on medical twitter: leveraging social media for disease detection. J. Med. Internet Res. (2023)"},{"key":"12_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Agarwal, D., Sun, J.: Medclip: contrastive learning from unpaired medical images and text (2022). https:\/\/arxiv.org\/abs\/2210.10163","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"12_CR31","doi-asserted-by":"crossref","unstructured":"Wu, C., Zhang, X., Zhang, Y., Wang, Y., Xie, W.: Medklip: medical knowledge enhanced language-image pre-training in radiology (2023). https:\/\/arxiv.org\/abs\/2301.02228","DOI":"10.1101\/2023.01.10.23284412"},{"key":"12_CR32","doi-asserted-by":"publisher","unstructured":"You, K.E.A.: Cxr-clip: toward large scale chest x-ray language-image pre-training. In: Greenspan, H.E.A. (ed.) MICCAI 2023. Lecture Notes in Computer Science, vol. 14221. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_10","DOI":"10.1007\/978-3-031-43895-0_10"},{"key":"12_CR33","doi-asserted-by":"publisher","first-page":"4542","DOI":"10.1038\/s41467-023-40260-7","volume":"14","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Wu, C., Zhang, Y., Xie, W., Wang, Y.: Knowledge-enhanced visual-language pre-training on chest radiology images. Nat. Commun. 14, 4542 (2023). https:\/\/doi.org\/10.1038\/s41467-023-40260-7","journal-title":"Nat. Commun."},{"key":"12_CR34","unstructured":"Zhang, Y., et\u00a0al.: Convirt. In: NeurIPS (2020). https:\/\/arxiv.org\/pdf\/2210.10163"}],"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\/978-3-032-04981-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T05:33:39Z","timestamp":1767418419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04981-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049803","9783032049810"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04981-0_12","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":"The authors have no competing interests to declare.","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"}}]}}