{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T13:40:20Z","timestamp":1743342020388,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819626434","type":"print"},{"value":"9789819626441","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-2644-1_8","type":"book-chapter","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T13:20:51Z","timestamp":1743340851000},"page":"105-117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Feature Aggregation Network Based on\u00a0Swin Transformer for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Hayato","family":"Iyoda","sequence":"first","affiliation":[]},{"given":"Yongqing","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5003-3180","authenticated-orcid":false,"given":"Xian-Hua","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,28]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Ailiang, L., Xu, J., Jinxing, L., Guangming, L.: Contrans: improving transformer with convolutional attention for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 297\u2013307 (2022)","DOI":"10.1007\/978-3-031-16443-9_29"},{"key":"8_CR2","unstructured":"Chen, J., et al.: Transunet: Transformers make strong encoders for medical image segmentation. CoRR (2021)"},{"key":"8_CR3","first-page":"424","volume":"9901","author":"O Cicek","year":"2016","unstructured":"Cicek, O., Abdulkadir, A., Lienkamp, S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. Med. Image Comput. Comput.-Assisted Interv. (MICCAI) 9901, 424\u2013432 (2016)","journal-title":"Med. Image Comput. Comput.-Assisted Interv. (MICCAI)"},{"key":"8_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1 (2019)"},{"key":"8_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2021)"},{"issue":"2","key":"8_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: Nnu-net: a self-conguring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"10","key":"8_CR7","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu, Z., et al.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281\u20132292 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR8","unstructured":"Han, K., Xiao, A., Wu, E., Guo, J., Xu, C., Wang, Y.: Transformer in transformer. CoRR (2021)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Hu, C., et al.: Swin-unet: Unet-like pure transformer for medical image segmentation. ECCV Computer Vision Workshop, pp. 205\u2013218 (2023)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Huang, H., et al.: Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP, pp. 1055\u20131059 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Ibtehaz, N., Rahman, M.S.: Rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121 (2020)","DOI":"10.1016\/j.neunet.2019.08.025"},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.3389\/fbioe.2020.605132","volume":"8","author":"Q Jin","year":"2020","unstructured":"Jin, Q., Meng, Z., Sun, C., Cui, H., Su, R.: Ra-unet: a hybrid deep attentionaware network to extract liver and tumor in ct scans. Front. Bioeng. Biotechnol. 8, 1471 (2020)","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"12","key":"8_CR14","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.W., Heng, P.A.: H-denseunet: hybrid densely connected unet for liver and tumor segmentation from ct volumes. IEEE Trans. Med. Imaging 37(12), 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: Hierarchical vision transformer using shifted windows. CoRR(2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Naik, S., Doyle, S., Agner, S., Madabhushi, A., Feldman, M., Tomaszewski, J.: Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In:5th IEEE International Symposium in Biomedical Imaging: From Nano to Macro, pp. 284\u2013287 (2008)","DOI":"10.1109\/ISBI.2008.4540988"},{"key":"8_CR18","unstructured":"abd R.\u00a0Girshick, X.W., Gupta, A., He, K.: Non-local neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794\u20137803 (2018)"},{"key":"8_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Rouhi, R., Jafari, M., Kasaei, S., Keshavarzian, P.: Benign and malignant breast tumors classication based on region growing and cnn segmentation. Expert Syst. Appli. 42(3), 990\u20131002 (2015)","DOI":"10.1016\/j.eswa.2014.09.020"},{"key":"8_CR21","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1002\/mrd.22489","volume":"82","author":"J Schindelin","year":"2015","unstructured":"Schindelin, J., Rueden, C.T., Hiner, M.C., Eliceiri, K.W.: The imagej ecosystem: an open platform for biomedical image analysis. Mol. Reprod. Dev. 82, 518\u2013529 (2015)","journal-title":"Mol. Reprod. Dev."},{"key":"8_CR22","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_CR23","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jegou, H.:Training data-efficient image transformers & distillation through attention. CoRR (2020)","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Valanarasu, J.M.J., Oza, P., Hacihaliloglu, I., Patel, V.M.: Medical transformer: gated axial-attention for medical image segmentation. CoRR (2021)","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"8_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inform. Process. Syst. 30 (2017)"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O.: Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: AAAI Conference on Artificial Intelligence vol. 36(3), 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, C., Ding, M., Li, J., Yu, H., Zha, S.: Transbts: Multimodal brain tumor segmentation using transformer. CoRR (2021)","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. CoRR (2021)","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Z., Min, X., Shi, F., Jin, R., Nawrin, S., Yu, I., Nagatomi, R.: Smeswin unet: merging cnn and transformer for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), pp. 517\u2013526 (2022)","DOI":"10.1007\/978-3-031-16443-9_50"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 327\u2013331 (2018)","DOI":"10.1109\/ITME.2018.00080"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, H., Hu, Q.: Transfuse: fusing transformers and cnns for medical image segmentation. CoRR (2021)","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"8_CR32","series-title":"Lecture Notes in Computer Science","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., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2024 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-2644-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T13:21:06Z","timestamp":1743340866000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-2644-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819626434","9789819626441"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-2644-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"28 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}