{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:46:44Z","timestamp":1758268004987,"version":"3.44.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032058690","type":"print"},{"value":"9783032058706","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\/978-3-032-05870-6_1","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T11:28:46Z","timestamp":1758194926000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LTCXNet: Tackling Long-Tailed Multi-label Classification and\u00a0Racial Bias in\u00a0Chest X-Ray Analysis"],"prefix":"10.1007","author":[{"given":"Chin-Wei","family":"Huang","sequence":"first","affiliation":[]},{"given":"Chi-Yu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Mu-Yi","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Kuan-Chang","family":"Shih","sequence":"additional","affiliation":[]},{"given":"Shih-Chih","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Po-Chih","family":"Kuo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Assran, M., et al.: Self-supervised learning from images with a joint-embedding predictive architecture. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15619\u201315629 (2023)","DOI":"10.1109\/CVPR52729.2023.01499"},{"key":"1_CR2","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"4209","DOI":"10.1038\/s41598-022-07939-1","volume":"12","author":"A Castelnovo","year":"2022","unstructured":"Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I.G., Cosentini, A.C.: A clarification of the nuances in the fairness metrics landscape. Sci. Rep. 12(1), 4209 (2022)","journal-title":"Sci. Rep."},{"key":"1_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.091","volume":"163","author":"F Charte","year":"2015","unstructured":"Charte, F., Rivera, A.J., Jesus, M.J., Herrera, F.: Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163, 3\u201316 (2015)","journal-title":"Neurocomputing"},{"key":"1_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"1_CR7","unstructured":"Chen, X., et al.: Symbolic discovery of optimization algorithms (2023). https:\/\/arxiv.org\/abs\/2302.06675"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"1_CR9","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88, 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"},{"issue":"6","key":"1_CR11","doi-asserted-by":"publisher","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","volume":"4","author":"JW Gichoya","year":"2022","unstructured":"Gichoya, J.W., et al.: AI recognition of patient race in medical imaging: a modelling study. Lancet Digit. Health 4(6), e406\u2013e414 (2022)","journal-title":"Lancet Digit. Health"},{"key":"1_CR12","doi-asserted-by":"publisher","unstructured":"Holste, G., et\u00a0al.: How does pruning impact long-tailed multi-label medical image classifiers? In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14224, pp. 663\u2013673. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43904-9_64","DOI":"10.1007\/978-3-031-43904-9_64"},{"key":"1_CR13","first-page":"19","volume":"5","author":"G Holste","year":"2023","unstructured":"Holste, G., et al.: CXR-LT: multi-label long-tailed classification on chest X-rays. PhysioNet 5, 19 (2023)","journal-title":"PhysioNet"},{"key":"1_CR14","doi-asserted-by":"publisher","unstructured":"Holste, G., et al.: Long-tailed classification of thorax diseases on chest X-ray: a new benchmark study. In: Nguyen, H.V., Huang, S.X., Xue, Y. (eds.) DALI 2022. LNCS, vol. 13567, 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":"1_CR15","doi-asserted-by":"crossref","unstructured":"Jeong, J., Jeoun, B., Park, Y., Han, B.: An optimized ensemble framework for multi-label classification on long-tailed chest X-ray data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2739\u20132746 (2023)","DOI":"10.1109\/ICCVW60793.2023.00289"},{"issue":"4","key":"1_CR16","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.116.005093","volume":"6","author":"H K\u00e4lsch","year":"2017","unstructured":"K\u00e4lsch, H., et al.: Aortic calcification onset and progression: association with the development of coronary atherosclerosis. J. Am. Heart Assoc. 6(4), e005093 (2017)","journal-title":"J. Am. Heart Assoc."},{"key":"1_CR17","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. arXiv preprint arXiv:1910.09217 (2019)"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Lanchantin, J., Wang, T., Ordonez, V., Qi, Y.: General multi-label image classification with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16478\u201316488 (2021)","DOI":"10.1109\/CVPR46437.2021.01621"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Li, B., Han, Z., Li, H., Fu, H., Zhang, C.: Trustworthy long-tailed classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6970\u20136979 (2022)","DOI":"10.1109\/CVPR52688.2022.00684"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1_CR22","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"1_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102820","volume":"68","author":"A Rath","year":"2021","unstructured":"Rath, A., Mishra, D., Panda, G., Satapathy, S.C.: Heart disease detection using deep learning methods from imbalanced ECG samples. Biomed. Signal Process. Control 68, 102820 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"1_CR24","unstructured":"Ren, J., Yu, C., Ma, X., Zhao, H., Yi, S., et al.: Balanced meta-softmax for long-tailed visual recognition. In: Advances in Neural Information Processing Systems, vol. 33, pp. 4175\u20134186 (2020)"},{"key":"1_CR25","doi-asserted-by":"crossref","unstructured":"Ridnik, T., Sharir, G., Ben-Cohen, A., Ben-Baruch, E., Noy, A.: ML-Decoder: scalable and versatile classification head. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 32\u201341 (2023)","DOI":"10.1109\/WACV56688.2023.00012"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Song, Y., Zhou, Q., Hu, K., Ma, L., Lu, X.: CFRL: coarse-fine decoupled representation learning for long-tailed recognition. In: Proceedings of the 6th ACM International Conference on Multimedia in Asia, pp.\u00a01\u20137 (2024)","DOI":"10.1145\/3696409.3700195"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Subramanian, S., Rahimi, A., Baldwin, T., Cohn, T., Frermann, L.: Fairness-aware class imbalanced learning. arXiv preprint arXiv:2109.10444 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.155"},{"key":"1_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107965","volume":"118","author":"AN Tarekegn","year":"2021","unstructured":"Tarekegn, A.N., Giacobini, M., Michalak, K.: A review of methods for imbalanced multi-label classification. Pattern Recogn. 118, 107965 (2021)","journal-title":"Pattern Recogn."},{"key":"1_CR30","unstructured":"Wang, Z., Liu, J.C.: Translating math formula images to latex sequences using deep neural networks with sequence-level training (2019)"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Yan, S., Kao, H.T., Ferrara, E.: Fair class balancing: enhancing model fairness without observing sensitive attributes. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1715\u20131724 (2020)","DOI":"10.1145\/3340531.3411980"}],"container-title":["Lecture Notes in Computer Science","Fairness of AI in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05870-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:06:44Z","timestamp":1758233204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05870-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032058690","9783032058706"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05870-6_1","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":"FAIMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Fairness of AI in Medical Imaging","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":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"faimi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/faimi-workshop.github.io\/2025-miccai-workshop\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}