{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T04:43:37Z","timestamp":1758343417747,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051684","type":"print"},{"value":"9783032051691","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\/978-3-032-05169-1_4","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:49:07Z","timestamp":1758318547000},"page":"34-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Background-Invariant Independence-Guided Multi-head Attention Network for\u00a0Skin Lesion Classification"],"prefix":"10.1007","author":[{"given":"Debasmit","family":"Roy","sequence":"first","affiliation":[]},{"given":"Srinjoy","family":"Dutta","sequence":"additional","affiliation":[]},{"given":"Soham","family":"Bose","sequence":"additional","affiliation":[]},{"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[]},{"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Bian, Y., Huang, J., Cai, X., Yuan, J., Church, K.: On attention redundancy: a comprehensive study. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 930\u2013945 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.72"},{"issue":"1","key":"4_CR2","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TETCI.2023.3309626","volume":"8","author":"B Chen","year":"2023","unstructured":"Chen, B., Liu, Y., Zhang, Z., Lu, G., Kong, A.W.K.: Transattunet: multi-level attention-guided u-net with transformer for medical image segmentation. IEEE Trans. Emerg. Topics Comput. Intell. 8(1), 55\u201368 (2023)","journal-title":"IEEE Trans. Emerg. Topics Comput. Intell."},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhang, Z., Cheng, Y., Awadallah, A., Wang, Z.: The principle of diversity: training stronger vision transformers calls for reducing all levels of redundancy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12020\u201312030 (2022)","DOI":"10.1109\/CVPR52688.2022.01171"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Chen, Y.J., Hu, X., Shi, Y., Ho, T.Y.: Ame-cam: attentive multiple-exit cam for weakly supervised segmentation on mri brain tumor. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 173\u2013182. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_17","DOI":"10.1007\/978-3-031-43907-0_17"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Chu, Y., Lee, S., Oh, B., Yang, S.: Class-agnostic feature-learning-based deep-learning model for robust melanoma prediction. IEEE J. Biomed. Health Inf. (2025)","DOI":"10.1109\/JBHI.2025.3535536"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Codella, N.C., et\u00a0al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168\u2013172. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"4_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106447","volume":"212","author":"S Ding","year":"2021","unstructured":"Ding, S., et al.: Deep attention branch networks for skin lesion classification. Comput. Methods Programs Biomed. 212, 106447 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"4_CR8","unstructured":"Greenfeld, D., Shalit, U.: Robust learning with the Hilbert-schmidt independence criterion. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0119, pp. 3759\u20133768. PMLR (2020). https:\/\/proceedings.mlr.press\/v119\/greenfeld20a.html"},{"key":"4_CR9","unstructured":"Gretton, A., Fukumizu, K., Teo, C., Song, L., Sch\u00f6lkopf, B., Smola, A.: A kernel statistical test of independence. In: Platt, J., Koller, D., Singer, Y., Roweis, S. (eds.) Advances in Neural Information Processing Systems, vol.\u00a020. Curran Associates, Inc. (2007)"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"HaCohen, Y., Fattal, R., Lischinski, D.: Image upsampling via texture hallucination. In: 2010 IEEE International Conference on Computational Photography (ICCP), pp.\u00a01\u20138. IEEE (2010)","DOI":"10.1109\/ICCPHOT.2010.5585097"},{"issue":"1","key":"4_CR11","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1038\/s41597-024-03387-w","volume":"11","author":"C Hern\u00e1ndez-P\u00e9rez","year":"2024","unstructured":"Hern\u00e1ndez-P\u00e9rez, C., et al.: Bcn20000: Dermoscopic lesions in the wild. Sci. Data 11(1), 641 (2024)","journal-title":"Sci. Data"},{"key":"4_CR12","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"4_CR13","unstructured":"Khetan, A., Karnin, Z.: Prunenet: channel pruning via global importance. arXiv preprint arXiv:2005.11282 (2020)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Li, T., et al.: Targeted supervised contrastive learning for long-tailed recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6918\u20136928 (2022)","DOI":"10.1109\/CVPR52688.2022.00679"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P.: Fully convolutional neural networks for remote sensing image classification. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5071\u20135074. IEEE (2016)","DOI":"10.1109\/IGARSS.2016.7730322"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Nguyen-Duc, T., Le, T., Bammer, R., Zhao, H., Cai, J., Phung, D.: Cross-adversarial local distribution regularization for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 183\u2013194. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_18","DOI":"10.1007\/978-3-031-43907-0_18"},{"issue":"1","key":"4_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"key":"4_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102693","volume":"84","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Wang, Y., Cai, J., Lee, T.K., Miao, C., Wang, Z.J.: SSD-KD: a self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images. Med. Image Anal. 84, 102693 (2023)","journal-title":"Med. Image Anal."},{"key":"4_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103549","volume":"74","author":"Z Wei","year":"2022","unstructured":"Wei, Z., Li, Q., Song, H.: Dual attention based network for skin lesion classification with auxiliary learning. Biomed. Signal Process. Control 74, 103549 (2022)","journal-title":"Biomed. Signal Process. Control"},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision - ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision - ECCV 2018, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Xu, G., Liu, Z., Li, X., Loy, C.C.: Knowledge distillation meets self-supervision. In: European Conference on Computer Vision, pp. 588\u2013604. Springer, Heidelberg (2020). https:\/\/doi.org\/10.1007\/s11263-024-02192-7","DOI":"10.1007\/s11263-024-02192-7"},{"key":"4_CR22","doi-asserted-by":"publisher","unstructured":"Xu, Y., Xie, S., Reynolds, M., Ragoza, M., Gong, M., Batmanghelich, K.: Adversarial consistency for single domain generalization in medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 671\u2013681. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-16449-1_64","DOI":"10.1007\/978-3-031-16449-1_64"},{"issue":"5","key":"4_CR23","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/TMI.2021.3136682","volume":"41","author":"P Yao","year":"2021","unstructured":"Yao, P., et al.: Single model deep learning on imbalanced small datasets for skin lesion classification. IEEE Trans. Med. Imaging 41(5), 1242\u20131254 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"4_CR24","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","volume":"38","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Xie, Y., Xia, Y., Shen, C.: Attention residual learning for skin lesion classification. IEEE Trans. Med. Imaging 38(9), 2092\u20132103 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR25","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Chen, J., Wang, K., Xie, F.: Ecl: class-enhancement contrastive learning for long-tailed skin lesion classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 244\u2013254. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-43895-0_23","DOI":"10.1007\/978-3-031-43895-0_23"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, C., Ni, B., Zhang, J., Zhao, Q., Zhang, W., Tian, Q.: Variational convolutional neural network pruning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2780\u20132789 (2019)","DOI":"10.1109\/CVPR.2019.00289"}],"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\/978-3-032-05169-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:49:14Z","timestamp":1758318554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05169-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032051684","9783032051691"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05169-1_4","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 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"}}]}}