{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:31:12Z","timestamp":1742974272180,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819983872"},{"type":"electronic","value":"9789819983889"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"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\/978-981-99-8388-9_13","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T16:02:21Z","timestamp":1701014541000},"page":"153-164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Cross Domain Pulmonary Nodule Detection Without Source Data"],"prefix":"10.1007","author":[{"given":"Rui","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yong","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Cai, Q., Pan, Y., Ngo, C., Tian, X., Duan, L., Yao, T.: Exploring object relation in mean teacher for cross-domain detection. In: CVPR, pp. 11457\u201311466. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.01172"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., Gool, L.V.: Domain adaptive faster R-CNN for object detection in the wild. In: CVPR, pp. 3339\u20133348. Computer Vision Foundation\/IEEE (2018)","DOI":"10.1109\/CVPR.2018.00352"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Girshick, R.B.: Fast R-CNN, In: ICCV. pp. 1440\u20131448. IEEE (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778. IEEE (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"He, M., et al.: Cross domain object detection by target-perceived dual branch distillation. In: CVPR, pp. 9560\u20139570. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00935"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"He, Y., Zhu, C., Wang, J., Savvides, M., Zhang, X.: Bounding box regression with uncertainty for accurate object detection. In: CVPR, pp. 2888\u20132897. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00300"},{"key":"13_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/978-3-030-58586-0_19","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z He","year":"2020","unstructured":"He, Z., Zhang, L.: Domain adaptive object detection via asymmetric tri-way faster-RCNN. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 309\u2013324. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58586-0_19"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Hofmanninger, J., Prayer, F., Pan, J., Rohrich, S., Prosch, H., Langs, G.: Automatic lung segmentation in routine imaging is a data diversity problem, not a methodology problem. CoRR abs\/2001.11767 (2020)","DOI":"10.1186\/s41747-020-00173-2"},{"key":"13_CR9","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, vol. 37, pp. 448\u2013456 (2015)"},{"key":"13_CR10","unstructured":"Iwasawa, Y., Matsuo, Y.: Test-time classifier adjustment module for model-agnostic domain generalization. In: NeurIPS, pp. 2427\u20132440 (2021)"},{"issue":"1","key":"13_CR11","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TCBB.2019.2963873","volume":"18","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y., et al.: A novel negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space and its application to brain CT image segmentation. IEEE ACM Trans. Comput. Biol. Bioinform. 18(1), 40\u201352 (2021)","journal-title":"IEEE ACM Trans. Comput. Biol. Bioinform."},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Khodabandeh, M., Vahdat, A., Ranjbar, M., Macready, W.G.: A robust learning approach to domain adaptive object detection. In: ICCV, pp. 480\u2013490. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00057"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., et al.: Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. In: NeurIPS (2020)","DOI":"10.1109\/CVPR46437.2021.01146"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Li, Y., Wang, N., Shi, J., Liu, J., Hou, X.: Revisiting batch normalization for practical domain adaptation. In: ICLR (2017)","DOI":"10.1016\/j.patcog.2018.03.005"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S.J.: Feature pyramid networks for object detection. In: CVPR, pp. 936\u2013944. IEEE (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2999\u20133007. IEEE (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"13_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"13_CR18","first-page":"4374","volume":"44","author":"J Mei","year":"2021","unstructured":"Mei, J., Cheng, M.M., Xu, G., Wan, L.R., Zhang, H.: SANet: a slice-aware network for pulmonary nodule detection. IEEE Trans. Pattern Anal. Mach. Intell. 44, 4374\u20134387 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR19","unstructured":"Morosov, S., et al.: Tagged results of lung computed tomography scans (RU 2018620500) (2018)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Qiu, H., Li, H., Wu, Q., Shi, H.: Offset bin classification network for accurate object detection. In: CVPR, pp. 13185\u201313194. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01320"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779\u2013788. IEEE (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"13_CR22","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":"13_CR23","doi-asserted-by":"crossref","unstructured":"Saito, K., Ushiku, Y., Harada, T., Saenko, K.: Strong-weak distribution alignment for adaptive object detection. In: CVPR, pp. 6956\u20136965. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00712"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Setio, A.A.A., et al.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med. Image Anal. 42, 1\u201313 (2017)","DOI":"10.1016\/j.media.2017.06.015"},{"issue":"3","key":"13_CR25","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379\u2013423 (1948)","journal-title":"Bell Syst. Tech. J."},{"key":"13_CR26","unstructured":"Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A.A., Hardt, M.: Test-time training with self-supervision for generalization under distribution shifts. In: ICML, vol. 119, pp. 9229\u20139248. PMLR (2020)"},{"key":"13_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-3-030-32226-7_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Tang","year":"2019","unstructured":"Tang, H., Zhang, C., Xie, X.: NoduleNet: decoupled false positive reduction for pulmonary nodule detection and segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 266\u2013274. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_30"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: ICCV, pp. 9626\u20139635. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"13_CR29","unstructured":"Tianchi: Tianchi medical AI competition: Intelligent diagnosis of pulmonary nodules (2017). https:\/\/tianchi.aliyun.com\/competition\/entrance\/231601\/introduction"},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"Tychsen-Smith, L., Petersson, L.: Improving object localization with fitness NMS and bounded IOU loss. In: CVPR, pp. 6877\u20136885. Computer Vision Foundation\/IEEE (2018)","DOI":"10.1109\/CVPR.2018.00719"},{"key":"13_CR31","unstructured":"Wang, D., Shelhamer, E., Liu, S., Olshausen, B.A., Darrell, T.: TENT: fully test-time adaptation by entropy minimization. In: ICLR (2021)"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Xu, C., Zhao, X., Jin, X., Wei, X.: Exploring categorical regularization for domain adaptive object detection. In: CVPR, pp. 11721\u201311730. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01174"},{"key":"13_CR33","doi-asserted-by":"publisher","unstructured":"Xu, R., et al.: SGDA: towards 3D universal pulmonary nodule detection via slice grouped domain attention. IEEE\/ACM Trans. Comput. Biol. Bioinform. 1\u201313 (2023). https:\/\/doi.org\/10.1109\/TCBB.2023.3253713","DOI":"10.1109\/TCBB.2023.3253713"},{"key":"13_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1007\/978-3-031-16431-6_63","volume-title":"Medical Image Computing and Computer Assisted Intervention - MICCAI 2022","author":"R Xu","year":"2022","unstructured":"Xu, R., Luo, Y., Du, B., Kuang, K., Yang, J.: LSSANet: a long short slice-aware network for pulmonary nodule detection. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13431, pp. 664\u2013674. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16431-6_63"},{"key":"13_CR35","unstructured":"Yang, T., Zhou, S., Wang, Y., Lu, Y., Zheng, N.: Test-time batch normalization. CoRR abs\/2205.10210 (2022)"},{"key":"13_CR36","unstructured":"You, F., Li, J., Zhao, Z.: Test-time batch statistics calibration for covariate shift. CoRR abs\/2110.04065 (2021)"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: CVPR, pp. 9756\u20139765. IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, Z., Mao, Y.: RPN prototype alignment for domain adaptive object detector. In: CVPR, pp. 12425\u201312434. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01224"},{"key":"13_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-030-58523-5_6","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Zhao","year":"2020","unstructured":"Zhao, G., Li, G., Xu, R., Lin, L.: Collaborative training between region proposal localization and classification for domain adaptive object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12363, pp. 86\u2013102. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58523-5_6"}],"container-title":["Lecture Notes in Computer Science","AI 2023: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8388-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T10:43:46Z","timestamp":1730630626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8388-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9789819983872","9789819983889"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8388-9_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brisbane, QLD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ajcai2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"213","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"11% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}