{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T18:59:49Z","timestamp":1757617189977,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":45,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784950"},{"type":"electronic","value":"9789819784967"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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\/978-981-97-8496-7_36","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T02:02:22Z","timestamp":1730512942000},"page":"518-532","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Frequency and Structure in UDA for Medical Object Detection"],"prefix":"10.1007","author":[{"given":"Liwen","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guannan","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengli","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Pu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Sha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingbo","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"issue":"1","key":"36_CR1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","volume":"4","author":"R Aggarwal","year":"2021","unstructured":"Aggarwal, R., Sounderajah, V., Martin, G., Ting, D.S., Karthikesalingam, A., King, D., Ashrafian, H., Darzi, A.: Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit. Med. 4(1), 65 (2021)","journal-title":"NPJ Digit. Med."},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT, pp. 177\u2013186. Springer (2010)","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Cai, M., Zhang, H., Huang, H., Geng, Q., Li, Y., Huang, G.: Frequency domain image translation: More photo-realistic, better identity-preserving. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13930\u201313940 (2021)","DOI":"10.1109\/ICCV48922.2021.01367"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Cao, S., Joshi, D., Gui, L.Y., Wang, Y.X.: Contrastive mean teacher for domain adaptive object detectors. In: Proceedings of CVPR, pp. 23839\u201323848 (2023)","DOI":"10.1109\/CVPR52729.2023.02283"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C., Zheng, Z., Ding, X., Huang, Y., Dou, Q.: Harmonizing transferability and discriminability for adapting object detectors. In: Proceedings of CVPR, pp. 8869\u20138878 (2020)","DOI":"10.1109\/CVPR42600.2020.00889"},{"key":"36_CR6","doi-asserted-by":"publisher","unstructured":"Dalca, A.V., Guttag, J., Sabuncu, M.R.: Anatomical priors in convolutional networks for unsupervised biomedical segmentation. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. IEEE (Jun 2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00968, http:\/\/dx.doi.org\/10.1109\/CVPR.2018.00968","DOI":"10.1109\/cvpr.2018.00968"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Li, W., Chen, Y., Duan, L.: Unbiased mean teacher for cross-domain object detection. In: Proceedings of CVPR, pp. 4091\u20134101 (2021)","DOI":"10.1109\/CVPR46437.2021.00408"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lin, K.Y., Yan, J., Wang, Y., Zheng, W.S.: Asyfod: an asymmetric adaptation paradigm for few-shot domain adaptive object detection. In: Proceedings of CVPR, pp. 3261\u20133271 (2023)","DOI":"10.1109\/CVPR52729.2023.00318"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Gao, Y., Yang, L., Huang, Y., Xie, S., Li, S., Zheng, W.S.: Acrofod: an adaptive method for cross-domain few-shot object detection. In: Proceedings of ECCV, pp. 673\u2013690. Springer (2022)","DOI":"10.1007\/978-3-031-19827-4_39"},{"issue":"3","key":"36_CR10","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","volume":"69","author":"H Guan","year":"2021","unstructured":"Guan, H., Liu, M.: Domain adaptation for medical image analysis: a survey. IEEE Trans. Biomed. Eng. 69(3), 1173\u20131185 (2021)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Hsu, H.K., Yao, C.H., Tsai, Y.H., Hung, W.C., Tseng, H.Y., Singh, M., Yang, M.H.: Progressive domain adaptation for object detection. In: Proceedings of WACV, pp. 749\u2013757 (2020)","DOI":"10.1109\/WACV45572.2020.9093358"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Huang, J., Guan, D., Xiao, A., Lu, S.: Fsdr: frequency space domain randomization for domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6891\u20136902 (2021)","DOI":"10.1109\/CVPR46437.2021.00682"},{"key":"36_CR14","first-page":"3635","volume":"34","author":"J Huang","year":"2021","unstructured":"Huang, J., Guan, D., Xiao, A., Lu, S.: Model adaptation: historical contrastive learning for unsupervised domain adaptation without source data. Adv. Neural. Inf. Process. Syst. 34, 3635\u20133649 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Jiang, L., Dai, B., Wu, W., Loy, C.C.: Focal frequency loss for image reconstruction and synthesis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13919\u201313929 (2021)","DOI":"10.1109\/ICCV48922.2021.01366"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Kim, S., Choi, J., Kim, T., Kim, C.: Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In: Proceedings of CVPR, pp. 6092\u20136101 (2019)","DOI":"10.1109\/ICCV.2019.00619"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Kim, T., Jeong, M., Kim, S., Choi, S., Kim, C.: Diversify and match: a domain adaptive representation learning paradigm for object detection. In: Proceedings of CVPR, pp. 12456\u201312465 (2019)","DOI":"10.1109\/CVPR.2019.01274"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, H., Li, J., Zhao, Z., Zhang, W., Zhang, S., Pu, S., Zhuang, Y., Wu, F.: Unsupervised domain adaptation for video object grounding with cascaded debiasing learning. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 3807\u20133816 (2023)","DOI":"10.1145\/3581783.3612314"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., Yuan, Y.: Sigma: semantic-complete graph matching for domain adaptive object detection. In: Proceedings of CVPR, pp. 5291\u20135300 (2022)","DOI":"10.1109\/CVPR52688.2022.00522"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Li, W., Liu, X., Yuan, Y.: Sigma++: improved semantic-complete graph matching for domain adaptive object detection. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2023.3235367"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y.J., Dai, X., Ma, C.Y., Liu, Y.C., Chen, K., Wu, B., He, Z., Kitani, K., Vajda, P.: Cross-domain adaptive teacher for object detection. In: Proceedings of CVPR, pp. 7581\u20137590 (2022)","DOI":"10.1109\/CVPR52688.2022.00743"},{"key":"36_CR22","unstructured":"Lu, Y., Li, K., Pu, B., Tan, Y., Zhu, N.: A yolox-based deep instance segmentation neural network for cardiac anatomical structures in fetal ultrasound images. IEEE\/ACM Trans. Comput. Biol. Bioinform. (2022)"},{"issue":"11","key":"36_CR23","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR24","doi-asserted-by":"crossref","unstructured":"Mattolin, G., Zanella, L., Ricci, E., Wang, Y.: Confmix: unsupervised domain adaptation for object detection via confidence-based mixing. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 423\u2013433 (2023)","DOI":"10.1109\/WACV56688.2023.00050"},{"key":"36_CR25","doi-asserted-by":"crossref","unstructured":"Ni, Z., Wu, J., Wang, Z., Yang, W., Wang, H., Ma, L.: Misalignment-robust frequency distribution loss for image transformation. arXiv preprint arXiv:2402.18192 (2024)","DOI":"10.1109\/CVPR52733.2024.00281"},{"key":"36_CR26","doi-asserted-by":"crossref","unstructured":"Oza, P., Sindagi, V.A., Sharmini, V.V., Patel, V.M.: Unsupervised domain adaptation of object detectors: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2023)","DOI":"10.1109\/TPAMI.2022.3217046"},{"key":"36_CR27","unstructured":"Paszke, A., Gross, S., Massa, et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. Proc. NeurIPS 32 (2019)"},{"key":"36_CR28","doi-asserted-by":"crossref","unstructured":"Piao, Z., Tang, L., Zhao, B.: Unsupervised domain-adaptive object detection via localization regression alignment. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3282958"},{"key":"36_CR29","unstructured":"Pu, B., Lv, X., Yang, J., Guannan, H., Dong, X., Lin, Y., Shengli, L., Ying, T., Fei, L., Chen, M., et\u00a0al.: Unsupervised domain adaptation for anatomical structure detection in ultrasound images. In: Forty-First International Conference on Machine Learning"},{"key":"36_CR30","doi-asserted-by":"crossref","unstructured":"Pu, B., Wang, L., Yang, J., He, G., Dong, X., Li, S., Tan, Y., Chen, M., Jin, Z., Li, K., et\u00a0al.: M3-uda: a new benchmark for unsupervised domain adaptive fetal cardiac structure detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621\u201311630 (2024)","DOI":"10.1109\/CVPR52733.2024.01104"},{"key":"36_CR31","unstructured":"Rodriguez, A.L., Mikolajczyk, K.: Domain adaptation for object detection via style consistency. arXiv preprint arXiv:1911.10033 (2019)"},{"key":"36_CR32","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Oza, P., Yasarla, R., Patel, V.M.: Prior-based domain adaptive object detection for hazy and rainy conditions. In: Proceedings of ECCV, pp. 763\u2013780. Springer (2020)","DOI":"10.1007\/978-3-030-58568-6_45"},{"key":"36_CR33","unstructured":"Stan, S., Rostami, M.: Domain adaptation for the segmentation of confidential medical images. arXiv preprint arXiv:2101.00522 (2021)"},{"key":"36_CR34","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fcos: fully convolutional one-stage object detection. In: Proceedings of CVPR, pp. 9627\u20139636 (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"36_CR35","doi-asserted-by":"crossref","unstructured":"Vs, V., Gupta, V., Oza, P., Sindagi, V.A., Patel, V.M.: Mega-cda: memory guided attention for category-aware unsupervised domain adaptive object detection. In: Proceedings of CVPR, pp. 4516\u20134526 (2021)","DOI":"10.1109\/CVPR46437.2021.00449"},{"key":"36_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, R., Zhang, S., Li, M., Xia, Y., Zhang, X., Liu, S.: Domain-specific suppression for adaptive object detection. In: Proceedings of CVPR, pp. 9603\u20139612 (2021)","DOI":"10.1109\/CVPR46437.2021.00948"},{"key":"36_CR37","doi-asserted-by":"crossref","unstructured":"Yang, J., Ding, X., Zheng, Z., Xu, X., Li, X.: Graphecho: graph-driven unsupervised domain adaptation for echocardiogram video segmentation. In: Proceedings of CVPR, pp. 11878\u201311887 (2023)","DOI":"10.1109\/ICCV51070.2023.01091"},{"key":"36_CR38","unstructured":"Yu, F., Wang, D., Chen, Y., Karianakis, N., Shen, T., Yu, P., Lymberopoulos, D., Lu, S., Shi, W., Chen, X.: Unsupervised domain adaptation for object detection via cross-domain semi-supervised learning. arXiv preprint arXiv:1911.07158 (2019)"},{"issue":"2","key":"36_CR39","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/jimaging7020031","volume":"7","author":"P Zhang","year":"2021","unstructured":"Zhang, P., Li, J., Wang, Y., Pan, J.: Domain adaptation for medical image segmentation: a meta-learning method. J. Imaging 7(2), 31 (2021)","journal-title":"J. Imaging"},{"key":"36_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chen, Q., Ng, R., Koltun, V.: Zoom to learn, learn to zoom. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3762\u20133770 (2019)","DOI":"10.1109\/CVPR.2019.00388"},{"key":"36_CR41","doi-asserted-by":"crossref","unstructured":"Zhao, G., Li, G., Xu, R., Lin, L.: Collaborative training between region proposal localization and classification for domain adaptive object detection. In: Proceedings of ECCV, pp. 86\u2013102. Springer (2020)","DOI":"10.1007\/978-3-030-58523-5_6"},{"key":"36_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, L., Tan, G., Wu, Q., Pu, B., Ren, H., Li, S., Li, K.: Farn: fetal anatomy reasoning network for detection with global context semantic and local topology relationship. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3392531"},{"key":"36_CR43","doi-asserted-by":"crossref","unstructured":"Zhao, L., Wang, L.: Task-specific inconsistency alignment for domain adaptive object detection. In: Proceedings of CVPR, pp. 14217\u201314226 (2022)","DOI":"10.1109\/CVPR52688.2022.01382"},{"key":"36_CR44","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Huang, D., Liu, S., Wang, Y.: Cross-domain object detection through coarse-to-fine feature adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13766\u201313775 (2020)","DOI":"10.1109\/CVPR42600.2020.01378"},{"key":"36_CR45","doi-asserted-by":"crossref","unstructured":"Zhou, M., Huang, J., Yan, K., Yu, H., Fu, X., Liu, A., Wei, X., Zhao, F.: Spatial-frequency domain information integration for pan-sharpening. In: European Conference on Computer Vision, pp. 274\u2013291. Springer (2022)","DOI":"10.1007\/978-3-031-19797-0_16"}],"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\/978-981-97-8496-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T23:44:54Z","timestamp":1757115894000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8496-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819784950","9789819784967"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8496-7_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","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":"Urumqi","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}