{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:58:14Z","timestamp":1775066294269,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721106","type":"print"},{"value":"9783031721113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_56","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"601-611","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["SelfReg-UNet: Self-Regularized UNet for\u00a0Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Wenhui","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Xiwen","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Peijie","family":"Qiu","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Farazi","sequence":"additional","affiliation":[]},{"given":"Aristeidis","family":"Sotiras","sequence":"additional","affiliation":[]},{"given":"Abolfazl","family":"Razi","sequence":"additional","affiliation":[]},{"given":"Yalin","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"56_CR1","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-031-25066-8_9","volume-title":"Computer Vision \u2013 ECCV 2022 Workshops: Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part III","author":"H Cao","year":"2023","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for\u00a0medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision \u2013 ECCV 2022 Workshops: Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part III, pp. 205\u2013218. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9"},{"key":"56_CR2","doi-asserted-by":"crossref","unstructured":"Chen, D., et al.: Cross-layer distillation with semantic calibration. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 7028\u20137036 (2021)","DOI":"10.1609\/aaai.v35i8.16865"},{"key":"56_CR3","unstructured":"Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"56_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-Decoder with Atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"56_CR5","unstructured":"Chung, I., Park, S., Kim, J., Kwak, N.: Feature-map-level online adversarial knowledge distillation. In: International Conference on Machine Learning, pp. 2006\u20132015. PMLR (2020)"},{"key":"56_CR6","doi-asserted-by":"crossref","unstructured":"Dalvi, F., Sajjad, H., Durrani, N., Belinkov, Y.: Analyzing redundancy in pretrained transformer models. arXiv preprint arXiv:2004.04010 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.398"},{"key":"56_CR7","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16$$\\,\\times \\,$$16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"56_CR8","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":"56_CR9","doi-asserted-by":"crossref","unstructured":"Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection CNNs by self attention distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1013\u20131021 (2019)","DOI":"10.1109\/ICCV.2019.00110"},{"issue":"7","key":"56_CR10","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.: A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans. Med. Imaging 36(7), 1550\u20131560 (2017). Jul","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"56_CR11","first-page":"249","volume":"35","author":"G Li","year":"2023","unstructured":"Li, G., Jin, D., Yu, Q., Qi, M.: IB-TransUNet: combining information bottleneck and transformer for medical image segmentation. J. King Saud Univ. Comput. Inf. Sci. 35(3), 249\u2013258 (2023)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"56_CR12","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: International Conference on Learning Representations (2017)"},{"key":"56_CR13","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-3-031-19809-0_20","volume-title":"Computer Vision \u2013 ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XXVI","author":"L Li","year":"2022","unstructured":"Li, L.: Self-regulated feature learning via\u00a0teacher-free feature distillation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision \u2013 ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part XXVI, pp. 347\u2013363. Springer Nature Switzerland, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19809-0_20"},{"key":"56_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, J., Shen, Z., Huang, G., Yan, S., Zhang, C.: Learning efficient convolutional networks through network slimming. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736\u20132744 (2017)","DOI":"10.1109\/ICCV.2017.298"},{"key":"56_CR15","unstructured":"Oktay, O., et\u00a0al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"issue":"1","key":"56_CR16","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1186\/s12859-023-05196-1","volume":"24","author":"S Pan","year":"2023","unstructured":"Pan, S., Liu, X., Xie, N., Chong, Y.: EG-TransuNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation. BMC Bioinform. 24(1), 85 (2023)","journal-title":"BMC Bioinform."},{"key":"56_CR17","unstructured":"Qiu, P., Yang, J., Kumar, S., Ghosh, S.S., Sotiras, A.: AgileFormer: spatially agile transformer UNet for medical image segmentation. arXiv preprint arXiv:2404.00122 (2024)"},{"key":"56_CR18","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Marculescu, R.: Medical image segmentation via cascaded attention decoding. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6222\u20136231 (2023)","DOI":"10.1109\/WACV56688.2023.00616"},{"key":"56_CR19","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: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III","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.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III, pp. 234\u2013241. Springer International Publishing, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"56_CR20","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"56_CR21","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K., et al.: Gland segmentation in colon histology images: the GlaS challenge contest. Med. Image Anal. 35, 489\u2013502 (2017)","journal-title":"Med. Image Anal."},{"key":"56_CR22","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: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I","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.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I, pp. 36\u201346. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_4"},{"key":"56_CR23","doi-asserted-by":"crossref","unstructured":"Wang, F.K., et al.: MRUNet: a two-stage segmentation model for small insect targets in complex environments. J. Integr. Agric. 22(4), 1117\u20131130 (2023)","DOI":"10.1016\/j.jia.2022.09.004"},{"key":"56_CR24","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O.R.: UCTransNet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"56_CR25","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"56_CR26","doi-asserted-by":"crossref","unstructured":"Wolchover, N.: New theory cracks open the black box of deep learning (2018)","DOI":"10.7551\/mitpress\/11909.003.0037"},{"key":"56_CR27","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/978-981-99-8543-2_4","volume-title":"Pattern Recognition and Computer Vision: 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13\u201315, 2023, Proceedings, Part VIII","author":"G Xu","year":"2024","unstructured":"Xu, G., Zhang, X., He, X., Wu, X.: LeViT-UNet: make faster encoders with\u00a0transformer for\u00a0medical image segmentation. In: Liu, Q., et al. (eds.) Pattern Recognition and Computer Vision: 6th Chinese Conference, PRCV 2023, Xiamen, China, October 13\u201315, 2023, Proceedings, Part VIII, pp. 42\u201353. Springer Nature Singapore, Singapore (2024). https:\/\/doi.org\/10.1007\/978-981-99-8543-2_4"},{"key":"56_CR28","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)"},{"key":"56_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, H., Tang, J., Hua, X.S., Sun, Q.: Self-regulation for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6953\u20136963 (2021)","DOI":"10.1109\/ICCV48922.2021.00687"},{"key":"56_CR30","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"},{"key":"56_CR31","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","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:07:01Z","timestamp":1728162421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_56","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}