{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:29:42Z","timestamp":1778894982569,"version":"3.51.4"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031460043","type":"print"},{"value":"9783031460050","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46005-0_8","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T04:01:36Z","timestamp":1696651296000},"page":"83-95","source":"Crossref","is-referenced-by-count":109,"title":["DAE-Former: Dual Attention-Guided Efficient Transformer for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Reza","family":"Azad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ren\u00e9","family":"Arimond","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ehsan Khodapanah","family":"Aghdam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amirhossein","family":"Kazerouni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dorit","family":"Merhof","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"8_CR1","unstructured":"Ali, A., et al.: XCiT: cross-covariance image transformers. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"issue":"1","key":"8_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-022-30695-9","volume":"13","author":"M Antonelli","year":"2022","unstructured":"Antonelli, M., et al.: The medical segmentation decathlon. Nat. Commun. 13(1), 1\u201313 (2022)","journal-title":"Nat. Commun."},{"key":"8_CR3","unstructured":"Azad, R., et al.: Medical image segmentation review: the success of U-Net. arXiv preprint arXiv:2211.14830 (2022)"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S.: Bi-directional ConvLSTM U-Net with densly connected convolutions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (2019)","DOI":"10.1109\/ICCVW.2019.00052"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Azad, R., et al.: TransDeepLab: convolution-free transformer-based DeepLab v3+ for medical image segmentation. In: Rekik, I., Adeli, E., Park, S.H., Cintas, C. (eds.) Predictive Intelligence in Medicine, PRIME 2022. LNCS, vol. 13564, pp. 91\u2013102. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16919-9_9","DOI":"10.1007\/978-3-031-16919-9_9"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Azad, R., Heidari, M., Wu, Y., Merhof, D.: Contextual attention network: transformer meets U-Net. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) Machine Learning in Medical Imaging, MLMI 2022. LNCS, vol. 13583, pp. 377\u2013386. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21014-3_39","DOI":"10.1007\/978-3-031-21014-3_39"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Azad, R., et al.: Advances in medical image analysis with vision transformers: a comprehensive review. arXiv preprint arXiv:2301.03505 (2023)","DOI":"10.1016\/j.media.2023.103000"},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Cao, H., et al.: Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision \u2013 ECCV 2022 Workshops, ECCV 2022. LNCS, vol. 13803, pp. 205\u2013218. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"8_CR9","unstructured":"Chen, C.F., Panda, R., Fan, Q.: RegionViT: regional-to-local attention for vision transformers. arXiv preprint arXiv:2106.02689 (2021)"},{"key":"8_CR10","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"8_CR11","unstructured":"Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Ding, M., Xiao, B., Codella, N., Luo, P., Wang, J., Yuan, L.: DaViT: dual attention vision transformers. arXiv preprint arXiv:2204.03645 (2022)","DOI":"10.1007\/978-3-031-20053-3_5"},{"key":"8_CR13","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","volume":"8","author":"MH Guo","year":"2022","unstructured":"Guo, M.H., et al.: Attention mechanisms in computer vision: a survey. Comput. Vis. Media 8, 331\u2013368 (2022)","journal-title":"Comput. Vis. Media"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Heidari, M., et al.: HiFormer: hierarchical multi-scale representations using transformers for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6202\u20136212 (2023)","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"8_CR16","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (GELUs). arXiv preprint arXiv:1606.08415 (2016)"},{"key":"8_CR17","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":"8_CR18","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TMI.2022.3230943","volume":"42","author":"X Huang","year":"2022","unstructured":"Huang, X., Deng, Z., Li, D., Yuan, X., Fu, Y.: MISSFormer: an effective transformer for 2D medical image segmentation. IEEE Trans. Med. Imaging 42, 1484\u20131494 (2022). https:\/\/doi.org\/10.1109\/TMI.2022.3230943","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Karaali, A., Dahyot, R., Sexton, D.J.: DR-VNet: retinal vessel segmentation via dense residual UNet. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds.) Pattern Recognition and Artificial Intelligence, ICPRAI 2022. LNCS, vol. 13363, pp. 198\u2013210. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-09037-0_17","DOI":"10.1007\/978-3-031-09037-0_17"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"8_CR21","unstructured":"Luo, H., Changdong, Y., Selvan, R.: Hybrid ladder transformers with efficient parallel-cross attention for medical image segmentation. In: International Conference on Medical Imaging with Deep Learning, pp. 808\u2013819. PMLR (2022)"},{"key":"8_CR22","unstructured":"Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"8_CR23","series-title":"Informatik aktuell","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-662-54345-0_3","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2017","author":"O Ronneberger","year":"2017","unstructured":"Ronneberger, O.: Invited Talk: U-Net convolutional networks for biomedical image segmentation. In: Bildverarbeitung f\u00fcr die Medizin 2017. I, pp. 3\u20133. Springer, Heidelberg (2017). https:\/\/doi.org\/10.1007\/978-3-662-54345-0_3"},{"key":"8_CR24","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., et al.: Attention gated networks: learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019)","journal-title":"Med. Image Anal."},{"key":"8_CR25","unstructured":"Shen, Z., Zhang, M., Zhao, H., Yi, S., Li, H.: Efficient attention: attention with linear complexities. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3531\u20133539 (2021)"},{"key":"8_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/978-3-030-87193-2_4","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","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: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 36\u201346. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_4"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Wang, H., et al.: Mixed transformer U-Net for medical image segmentation. In: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2022, pp. 2390\u20132394. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"key":"8_CR28","doi-asserted-by":"publisher","first-page":"102327","DOI":"10.1016\/j.media.2021.102327","volume":"76","author":"H Wu","year":"2022","unstructured":"Wu, H., Chen, S., Chen, G., Wang, W., Lei, B., Wen, Z.: FAT-Net: feature adaptive transformers for automated skin lesion segmentation. Med. Image Anal. 76, 102327 (2022)","journal-title":"Med. Image Anal."},{"key":"8_CR29","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 12077\u201312090 (2021)"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Xu, G., Wu, X., Zhang, X., He, X.: LeViT-UNet: make faster encoders with transformer for medical image segmentation. arXiv preprint arXiv:2107.08623 (2021)","DOI":"10.2139\/ssrn.4116174"},{"key":"8_CR31","unstructured":"Zhu, X., et al.: Region aware transformer for automatic breast ultrasound tumor segmentation. In: International Conference on Medical Imaging with Deep Learning, pp. 1523\u20131537. PMLR (2022)"}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46005-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T13:46:35Z","timestamp":1703339195000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46005-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031460043","9783031460050"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46005-0_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}