{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T15:44:05Z","timestamp":1775317445660,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":20,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556304","type":"print"},{"value":"9789819556311","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-5631-1_11","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T07:08:04Z","timestamp":1769497684000},"page":"148-161","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MoGaze: Momentum Gaze Contrastive Learning Framework for\u00a0Self-supervised Abdominal Multi-organ Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7274-0521","authenticated-orcid":false,"given":"Jianshan","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9086-7366","authenticated-orcid":false,"given":"Pinle","family":"Qin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9099-9695","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8042-713X","authenticated-orcid":false,"given":"Jinjing","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7755-7550","authenticated-orcid":false,"given":"Jianchao","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"11_CR1","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. Springer (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9640\u20139649 (2021)","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"11_CR3","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems, vol. 33, pp. 21271\u201321284 (2020)"},{"key":"11_CR4","doi-asserted-by":"publisher","unstructured":"He, J., et al.: TransFG: a transformer architecture for fine-grained recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 852\u2013860 (2022). https:\/\/doi.org\/10.1609\/aaai.v36i1.19967","DOI":"10.1609\/aaai.v36i1.19967"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Li, Z., Xing, Z., Liu, H., Zhu, L., Wan, L.: Anchored supervised contrastive learning for long-tailed medical image regression. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 3\u201318. Springer (2024)","DOI":"10.1007\/978-981-97-8499-8_1"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Liping, Q., Hua, W.: Formation mechanism and clinical application of the dominant eye. Int. J. Ophthalmol. Vis. Sci. 5(2), 47 (2020). https:\/\/doi.org\/10.11648\/j.ijovs.20200502.12","DOI":"10.11648\/j.ijovs.20200502.12"},{"key":"11_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110998","volume":"158","author":"X Long","year":"2025","unstructured":"Long, X., Peng, C., Li, Y.: MNN: mixed nearest-neighbors for self-supervised learning. Pattern Recogn. 158, 110998 (2025)","journal-title":"Pattern Recogn."},{"key":"11_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102642","volume":"82","author":"X Luo","year":"2022","unstructured":"Luo, X., et al.: Word: a large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image. Med. Image Anal. 82, 102642 (2022). https:\/\/doi.org\/10.1016\/j.media.2022.102642","journal-title":"Med. Image Anal."},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Ooi, T.L., He, Z.J.: Sensory eye dominance: relationship between eye and brain. Eye Brain, 25\u201331 (2020)","DOI":"10.2147\/EB.S176931"},{"key":"11_CR10","doi-asserted-by":"publisher","unstructured":"Seong, H.S., Moon, W., Lee, S., Heo, J.P.: Leveraging hidden positives for unsupervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19540\u201319549 (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.15014","DOI":"10.48550\/arXiv.2303.15014"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Wang, H., Qiu, L., Li, Y., Hu, J., Zhang, J.: Semi-supervised medical image segmentation with strong\/weak task-aware consistency. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 17\u201331. Springer (2024)","DOI":"10.1007\/978-981-97-8496-7_2"},{"issue":"1","key":"11_CR12","doi-asserted-by":"publisher","first-page":"9227348","DOI":"10.1155\/2023\/9227348","volume":"2023","author":"Q Wang","year":"2023","unstructured":"Wang, Q., et al.: Semi-white-box strategy: enhancing data efficiency and interpretability of convolutional neural networks in image processing. Int. J. Intell. Syst. 2023(1), 9227348 (2023). https:\/\/doi.org\/10.1155\/2023\/9227348","journal-title":"Int. J. Intell. Syst."},{"key":"11_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3373018","author":"W Wang","year":"2024","unstructured":"Wang, W., et al.: A two-stage generative model with CycleGAN and joint diffusion for MRI-based brain tumor detection. IEEE J. Biomed. Health Inform. (2024). https:\/\/doi.org\/10.1109\/JBHI.2024.3373018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"11_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102559","volume":"81","author":"X Wang","year":"2022","unstructured":"Wang, X., et al.: Transformer-based unsupervised contrastive learning for histopathological image classification. Med. Image Anal. 81, 102559 (2022). https:\/\/doi.org\/10.1016\/j.media.2022.102559","journal-title":"Med. Image Anal."},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Wu, L., Zhuang, J., Chen, H.: VoCo: a simple-yet-effective volume contrastive learning framework for 3D medical image analysis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22873\u201322882 (2024)","DOI":"10.1109\/CVPR52733.2024.02158"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3733\u20133742 (2018)","DOI":"10.1109\/CVPR.2018.00393"},{"key":"11_CR17","doi-asserted-by":"publisher","unstructured":"Xia, Q., et al.: A comprehensive review of deep learning for medical image segmentation. Neurocomputing, 128740 (2024). https:\/\/doi.org\/10.1016\/j.neucom.2024.128740","DOI":"10.1016\/j.neucom.2024.128740"},{"key":"11_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/mp.17128","author":"Z Xu","year":"2024","unstructured":"Xu, Z., Dai, Y., Liu, F., Wu, B., Chen, W., Shi, L.: Swin MoCo: improving parotid gland MRI segmentation using contrastive learning. Med. Phys. (2024). https:\/\/doi.org\/10.1002\/mp.17128","journal-title":"Med. Phys."},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Yang, D., Zhao, H., Jin, G., Meng, H., Zhang, L.: Class-aware cross pseudo supervision framework for semi-supervised multi-organ segmentation in abdominal CT scans. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 148\u2013162. Springer (2024)","DOI":"10.1007\/978-981-97-8496-7_11"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, J., Li, T., Jiang, D., Wu, S., Ramirez, A., Lee, T.S.: Perceptual inductive bias is what you need before contrastive learning. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 9621\u20139630 (2025)","DOI":"10.1109\/CVPR52734.2025.00899"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5631-1_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T14:44:20Z","timestamp":1775313860000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5631-1_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556304","9789819556311"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5631-1_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"28 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}