{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T08:02:35Z","timestamp":1784361755656,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234288","type":"print"},{"value":"9789819234295","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"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":[[2027]]},"DOI":"10.1007\/978-981-92-3429-5_26","type":"book-chapter","created":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T07:06:47Z","timestamp":1784358407000},"page":"312-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DCAU-Net: Differential Cross Attention and Channel-Spatial Feature Fusion for Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Yanxin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libin","family":"Lan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"26_CR1","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint https:\/\/arxiv.org\/abs\/2102.04306 (2021)"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Cao, H., , et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218 (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3d medical image segmentation. In: IEEE Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"26_CR4","first-page":"234","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer (2015)"},{"key":"26_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311. Springer (2018)"},{"key":"26_CR6","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint https:\/\/arxiv.org\/abs\/1804.03999 (2018)"},{"key":"26_CR7","first-page":"1055","volume-title":"IEEE International Conference on Acoustics, Speech and Signal Processing","author":"H Huang","year":"2020","unstructured":"Huang, H., et al.: UNet 3+: a full-scale connected Unet for medical image segmentation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1055\u20131059. IEEE (2020)"},{"key":"26_CR8","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2021)"},{"key":"26_CR9","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 6000\u20136010 (2017)"},{"key":"26_CR10","unstructured":"Ye, T., et al.: Differential transformer. In: International Conference on Learning Representations (2025)"},{"key":"26_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102634","volume":"113","author":"X Liu","year":"2025","unstructured":"Liu, X., et al.: CSWin-UNet: Transformer UNet with cross-shaped windows for medical image segmentation. Inf. Fusion. 113, 102634 (2025)","journal-title":"Inf. Fusion"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Heidari, M., et al.: HiFormer: hierarchical multi-scale representations using transformers for medical image segmentation. In: IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6202\u20136212 (2023)","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"26_CR13","first-page":"2291","volume-title":"IEEE International Conference on Multimedia and Expo","author":"H Zeng","year":"2023","unstructured":"Zeng, H., et al.: MSAANet: multi-scale axial attention network for medical image segmentation. In: IEEE International Conference on Multimedia and Expo, pp. 2291\u20132296. IEEE (2023)"},{"key":"26_CR14","first-page":"36","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"JMJ Valanarasu","year":"2021","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 36\u201346. Springer (2021)"},{"key":"26_CR15","unstructured":"Lan, L., et al.: BRAU-Net++: U-shaped hybrid CNN-transformer network for medical image segmentation. arXiv preprint https:\/\/arxiv.org\/abs\/2401.00722 (2024)"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Guo, P., Zeng, W., Xu, S.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: European Conference on Computer Vision, pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"26_CR19","unstructured":"Zhang, B., Sennrich, R.: Root mean square layer normalization. Adv. Neural Inf. Proces. Syst. 32 (2019)"},{"key":"26_CR20","unstructured":"Landman, B., et al.: Multi-atlas labeling beyond the cranial vault\u2013workshop and challenge. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Multi-Atlas Labeling Beyond Cranial Vault Workshop Challenge, Munich, Germany, p. 12 (2015)"},{"issue":"11","key":"26_CR21","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging. 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Marculescu, R.: Medical image segmentation via cascaded attention decoding. In: IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6222\u20136231 (2023)","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"26_CR23","unstructured":"Huang, X., et al.: MISSFormer: an effective medical image segmentation transformer. arXiv preprint https:\/\/arxiv.org\/abs\/2109.07162 (2021)"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Ruan, J., Li, J., Xiang, S.: VM-UNet: vision mamba U-Net for medical image segmentation. ACM Transactions on Multimedia Computing, Communications and Applications (2024)","DOI":"10.1145\/3767748"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3429-5_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T07:06:49Z","timestamp":1784358409000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3429-5_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"ISBN":["9789819234288","9789819234295"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3429-5_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"19 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}