{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:37:43Z","timestamp":1758281863199,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049643","type":"print"},{"value":"9783032049650","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\/978-3-032-04965-0_25","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T08:05:41Z","timestamp":1758182741000},"page":"263-273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Indepth Integration of\u00a0Multi-granularity Features from\u00a0Dual-modal for\u00a0Disease Classification"],"prefix":"10.1007","author":[{"given":"Yeli","family":"Wu","sequence":"first","affiliation":[]},{"given":"Xiaocai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Weiwen","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Haiteng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Chao","family":"An","sequence":"additional","affiliation":[]},{"given":"Jianjia","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"25_CR1","first-page":"20014","volume":"34","author":"A Ali","year":"2021","unstructured":"Ali, A., et al.: XCiT: cross-covariance image transformers. Adv. Neural. Inf. Process. Syst. 34, 20014\u201320027 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR2","unstructured":"An, C., et\u00a0al.: Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma. Eur. J. Nucl. Med. Mol. Imaging, 1\u201313 (2022)"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., J\u00e9gou, H., Douze, M.: Levit: a vision transformer in convnet\u2019s clothing for faster inference. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp. 12259\u201312269 (2021)","DOI":"10.1109\/ICCV48922.2021.01204"},{"key":"25_CR5","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Hashim, S., Ali, M., Nandakumar, K., Yaqub, M.: SubOmiEmbed: self-supervised representation learning of multi-omics data for cancer type classification. In: 2022 10th International Conference on Bioinformatics and Computational Biology (ICBCB), pp. 66\u201372. IEEE (2022)","DOI":"10.1109\/ICBCB55259.2022.9802478"},{"key":"25_CR7","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Kautz, J.: MambaVision: a hybrid mamba-transformer vision backbone. arXiv preprint arXiv:2407.08083 (2024)","DOI":"10.1109\/CVPR52734.2025.02352"},{"key":"25_CR8","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":"25_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108990","volume":"133","author":"X He","year":"2023","unstructured":"He, X., Wang, Y., Zhao, S., Chen, X.: Co-attention fusion network for multimodal skin cancer diagnosis. Pattern Recogn. 133, 108990 (2023)","journal-title":"Pattern Recogn."},{"key":"25_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105534","volume":"87","author":"X Huo","year":"2024","unstructured":"Huo, X., et al.: HiFuse: hierarchical multi-scale feature fusion network for medical image classification. Biomed. Signal Process. Control 87, 105534 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Kang, K., Xu, J., An, S., Chen, J., Wang, R., Wang, H.: Multimodal hybrid approach for fine-grained classification of diverse dermatological conditions. In: 2024 2nd International Conference on Intelligent Perception and Computer Vision (CIPCV), pp. 157\u2013164. IEEE (2024)","DOI":"10.1109\/CIPCV61763.2024.00035"},{"issue":"2","key":"25_CR12","doi-asserted-by":"publisher","first-page":"538","DOI":"10.1109\/JBHI.2018.2824327","volume":"23","author":"J Kawahara","year":"2018","unstructured":"Kawahara, J., Daneshvar, S., Argenziano, G., Hamarneh, G.: Seven-point checklist and skin lesion classification using multitask multimodal neural nets. IEEE J. Biomed. Health Inform. 23(2), 538\u2013546 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"25_CR13","first-page":"103031","volume":"37","author":"Y Liu","year":"2025","unstructured":"Liu, Y., et al.: VMamba: visual state space model. Adv. Neural. Inf. Process. Syst. 37, 103031\u2013103063 (2025)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"6","key":"25_CR14","doi-asserted-by":"publisher","first-page":"3699","DOI":"10.1364\/BOE.516764","volume":"15","author":"Z Liu","year":"2024","unstructured":"Liu, Z., et al.: Cross-modal attention network for retinal disease classification based on multi-modal images. Biomed. Opt. Express 15(6), 3699\u20133714 (2024)","journal-title":"Biomed. Opt. Express"},{"issue":"3","key":"25_CR15","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s40846-023-00801-3","volume":"43","author":"M Odusami","year":"2023","unstructured":"Odusami, M., Maskeli\u016bnas, R., Dama\u0161evi\u010dius, R., Misra, S.: Explainable deep-learning-based diagnosis of Alzheimer\u2019s disease using multimodal input fusion of pet and MRI images. J. Med. Biol. Eng. 43(3), 291\u2013302 (2023)","journal-title":"J. Med. Biol. Eng."},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Doll\u00e1r, P.: Designing network design spaces. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428\u201310436 (2020)","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"25_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106180","volume":"93","author":"C Shu","year":"2024","unstructured":"Shu, C., Yu, L., Tian, S., Shi, X.: MSMA: a multi-stage and multi-attention algorithm for the classification of multimodal skin lesions. Biomed. Signal Process. Control 93, 106180 (2024)","journal-title":"Biomed. Signal Process. Control"},{"key":"25_CR18","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"25_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102307","volume":"76","author":"P Tang","year":"2022","unstructured":"Tang, P., Yan, X., Nan, Y., Xiang, S., Krammer, S., Lasser, T.: FusionM4Net: a multi-stage multi-modal learning algorithm for multi-label skin lesion classification. Med. Image Anal. 76, 102307 (2022)","journal-title":"Med. Image Anal."},{"issue":"12","key":"25_CR20","doi-asserted-by":"publisher","first-page":"12623","DOI":"10.1109\/TCYB.2021.3069920","volume":"52","author":"S Wang","year":"2021","unstructured":"Wang, S., Yin, Y., Wang, D., Wang, Y., Jin, Y.: Interpretability-based multimodal convolutional neural networks for skin lesion diagnosis. IEEE Trans. Cybern. 52(12), 12623\u201312637 (2021)","journal-title":"IEEE Trans. Cybern."},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Xiao, C., et\u00a0al.: Attention-guided learning with feature reconstruction for skin lesion diagnosis using clinical and ultrasound images. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3450682"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Xu, J., et\u00a0al.: RemixFormer++: a multi-modal transformer model for precision skin tumor differential diagnosis with memory-efficient attention. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3441012"},{"issue":"5","key":"25_CR23","doi-asserted-by":"publisher","first-page":"3808","DOI":"10.1007\/s10489-023-05090-6","volume":"54","author":"A Yang","year":"2024","unstructured":"Yang, A., Xu, L., Qin, N., Huang, D., Liu, Z., Shu, J.: MFU-Net: a deep multimodal fusion network for breast cancer segmentation with dual-layer spectral detector CT. Appl. Intell. 54(5), 3808\u20133824 (2024)","journal-title":"Appl. Intell."},{"key":"25_CR24","first-page":"15475","volume":"34","author":"Q Zhang","year":"2021","unstructured":"Zhang, Q., Yang, Y.B.: ResT: an efficient transformer for visual recognition. Adv. Neural. Inf. Process. Syst. 34, 15475\u201315485 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"25_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106712","volume":"157","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Xie, F., Chen, J.: TFormer: a throughout fusion transformer for multi-modal skin lesion diagnosis. Comput. Biol. Med. 157, 106712 (2023)","journal-title":"Comput. Biol. Med."},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhou, J., Qian, W., Tang, S., Chang, X., Guo, D.: Dense audio-visual event localization under cross-modal consistency and multi-temporal granularity collaboration. arXiv preprint arXiv:2412.12628 (2024)","DOI":"10.1609\/aaai.v39i10.33185"}],"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\/978-3-032-04965-0_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T22:03:16Z","timestamp":1758232996000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04965-0_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032049643","9783032049650"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04965-0_25","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"}}]}}