{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T01:25:14Z","timestamp":1775784314685,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":46,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819619061","type":"print"},{"value":"9789819619078","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\/978-981-96-1907-8_24","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:54:57Z","timestamp":1740426897000},"page":"243-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Domain Adaptation for Semantic Segmentation of Cataract Surgical Images Based on Masked Image Consistency"],"prefix":"10.1007","author":[{"given":"Yuzhu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijie","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyang","family":"Ou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanghui","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinhu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"issue":"7","key":"24_CR1","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1109\/TMI.2022.3147854","volume":"41","author":"H Li","year":"2022","unstructured":"Li, H., et al.: An annotation-free restoration network for cataractous fundus images. IEEE Trans. Med. Imaging 41(7), 1699\u20131710 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, M., et al.: Integrating intra-phase coherence and intra-frame scene perception for panoptic segmentation in cataract surgery video. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2024)","DOI":"10.1109\/ISBI56570.2024.10635465"},{"key":"24_CR3","unstructured":"Ghamsarian, N., et al.: Cataract-1K: cataract surgery dataset for scene segmentation, phase recognition, and irregularity detection. arXiv preprint arXiv:2312.06295 (2023)"},{"key":"24_CR4","unstructured":"Grammatikopoulou, M., et al.: Cadis: cataract dataset for image segmentation. arXiv preprint arXiv:1906.11586 (2019)"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Wang, H., Van Gool, L.: MIC: masked image consistency for context-enhanced domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11721\u201311732 (2023)","DOI":"10.1109\/CVPR52729.2023.01128"},{"key":"24_CR6","unstructured":"Li, H., et al.: RaffeSDG: random frequency filtering enabled single-source domain generalization for medical image segmentation. arXiv preprint arXiv:2405.01228 (2024)"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Chen, M., Xue, H., Cai, D.: Domain adaptation for semantic segmentation with maximum squares loss. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2090\u20132099 (2019)","DOI":"10.1109\/ICCV.2019.00218"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Cai, Q., Pan, Y., Ngo, C.W., Tian, X., Duan, L., Yao, T.: Exploring object relation in mean teacher for cross-domain detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11457\u201311466 (2019)","DOI":"10.1109\/CVPR.2019.01172"},{"key":"24_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.media.2018.11.008","volume":"52","author":"H Al Hajj","year":"2019","unstructured":"Al Hajj, H., et al.: CATARACTS: challenge on automatic tool annotation for cataRACT surgery. Med. Image Anal. 52, 24\u201341 (2019)","journal-title":"Med. Image Anal."},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Fox, M., Taschwer, M., Schoeffmann, K.: Pixel-based tool segmentation in cataract surgery videos with mask R-CNN. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 565\u2013568. IEEE (2020)","DOI":"10.1109\/CBMS49503.2020.00112"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Li, H., Li, H., Shu, H., Chen, J., Hu, Y., Liu, J.: Self-supervision boosted retinal vessel segmentation for cross-domain data. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230561"},{"key":"24_CR12","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)"},{"issue":"4","key":"24_CR13","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"24_CR15","unstructured":"Loshchilov, I.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"24_CR16","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Van Gool, L.: Daformer: improving network architectures and training strategies for domain-adaptive semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9924\u20139935 (2022)","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"24_CR18","doi-asserted-by":"publisher","unstructured":"Hoyer, L., Dai, D., Van Gool, L.: HRDA: context-aware high-resolution domain-adaptive semantic segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13690, pp. 372\u2013391. Springe, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20056-4_22","DOI":"10.1007\/978-3-031-20056-4_22"},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881\u20132890 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Ou, M., et al.: MVD-Net: semantic segmentation of cataract surgery using multi-view learning. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5035\u20135038. IEEE (2022)","DOI":"10.1109\/EMBC48229.2022.9871673"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"He, J., Deng, Z., Zhou, L., Wang, Y., Qiao, Y.: Adaptive pyramid context network for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7519\u20137528 (2019)","DOI":"10.1109\/CVPR.2019.00770"},{"key":"24_CR22","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"24_CR23","doi-asserted-by":"publisher","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"24_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Lin, Y., et al.: Domain adaptative retinal image quality assessment with knowledge distillation using competitive teacher-student network. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230455"},{"key":"24_CR26","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"24_CR27","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"24_CR28","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz, N., Rahman, M.S.: MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74\u201387 (2020)","journal-title":"Neural Netw."},{"issue":"10","key":"24_CR29","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","volume":"39","author":"S Feng","year":"2020","unstructured":"Feng, S., et al.: CPFNet: context pyramid fusion network for medical image segmentation. IEEE Trans. Med. Imaging 39(10), 3008\u20133018 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"24_CR30","doi-asserted-by":"publisher","unstructured":"Li, H., et al.: Frequency-mixed single-source domain generalization for medical image segmentation. In: Greenspan, H., et al. (eds.) MICCAI 2023. LNCS, vol. 14225, pp. 127\u2013136. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43987-2_13","DOI":"10.1007\/978-3-031-43987-2_13"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Li, H., Lin, Z., Qiu, Z., et al.: Enhancing and adapting in the clinic: Source-free unsupervised domain adaptation for medical image enhancement. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3335651"},{"issue":"59","key":"24_CR32","first-page":"1","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1\u201335 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR33","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"issue":"10","key":"24_CR34","doi-asserted-by":"publisher","first-page":"2865","DOI":"10.1007\/s11263-021-01496-2","volume":"129","author":"R Gong","year":"2021","unstructured":"Gong, R., Li, W., Chen, Y., Dai, D., Van Gool, L.: Dlow: domain flow and applications. Int. J. Comput. Vision 129(10), 2865\u20132888 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"24_CR35","unstructured":"Hoffman, J., et al.: Cycada: cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning, pp. 1989\u20131998. PMLR (2018)"},{"key":"24_CR36","unstructured":"Hoffman, J., Wang, D., Yu, F., Darrell, T.: FCNs in the wild: pixel-level adversarial and constraint-based adaptation. arXiv preprint arXiv:1612.02649 (2016)"},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7472\u20137481 (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"key":"24_CR38","doi-asserted-by":"publisher","unstructured":"Mei, K., Zhu, C., Zou, J., Zhang, S.: Instance adaptive self-training for unsupervised domain adaptation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) ECCV 2020, Part XXVI. LNCS, vol. 12371, pp. 415\u2013430. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58574-7_25","DOI":"10.1007\/978-3-030-58574-7_25"},{"key":"24_CR39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Deng, J., Gao, X., Li, W., Duan, L.: Bapa-net: boundary adaptation and prototype alignment for cross-domain semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8801\u20138811 (2021)","DOI":"10.1109\/ICCV48922.2021.00868"},{"key":"24_CR40","unstructured":"Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. In: Advances in Neural Information Processing Systems, vol. 33, pp. 596\u2013608 (2020)"},{"key":"24_CR41","doi-asserted-by":"crossref","unstructured":"Melas-Kyriazi, L., Manrai, A.K.: Pixmatch: unsupervised domain adaptation via pixelwise consistency training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12435\u201312445 (2021)","DOI":"10.1109\/CVPR46437.2021.01225"},{"issue":"2","key":"24_CR42","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/TCSVT.2022.3206476","volume":"33","author":"Q Zhou","year":"2022","unstructured":"Zhou, Q., et al.: Context-aware mixup for domain adaptive semantic segmentation. IEEE Trans. Circuits Syst. Video Technol. 33(2), 804\u2013817 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"10","key":"24_CR43","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.1007\/s11548-023-02906-1","volume":"18","author":"Y Wang","year":"2023","unstructured":"Wang, Y., et al.: CGBA-Net: context-guided bidirectional attention network for surgical instrument segmentation. Int. J. Comput. Assist. Radiol. Surg. 18(10), 1769\u20131781 (2023)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"8","key":"24_CR44","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1007\/s11263-023-01799-6","volume":"131","author":"L Hoyer","year":"2023","unstructured":"Hoyer, L., Dai, D., Wang, Q., Chen, Y., Van Gool, L.: Improving semi-supervised and domain-adaptive semantic segmentation with self-supervised depth estimation. Int. J. Comput. Vision 131(8), 2070\u20132096 (2023)","journal-title":"Int. J. Comput. Vision"},{"key":"24_CR45","unstructured":"Zhang, K., Sun, Y., Wang, R., Li, H., Hu, X.: Multiple fusion adaptation: a strong framework for unsupervised semantic segmentation adaptation. arXiv preprint arXiv:2112.00295 (2021)"},{"key":"24_CR46","doi-asserted-by":"publisher","unstructured":"Li, B., et al.: FD-SDG: frequency dropout based single source domain generalization framework for retinal vessel segmentation. In: Huang, D.S., Zhang, Q., Guo, J. (eds.) ICIC 2024. LNCS, vol. 14881, pp. 393\u2013404. Springer, Singapore (2024). https:\/\/doi.org\/10.1007\/978-981-97-5689-6_34","DOI":"10.1007\/978-981-97-5689-6_34"}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1907-8_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:55:12Z","timestamp":1740426912000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1907-8_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819619061","9789819619078"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1907-8_24","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhenzhou","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.org.cn\/2024\/Organization.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}