{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T10:24:47Z","timestamp":1758450287061,"version":"3.44.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049261"},{"type":"electronic","value":"9783032049278"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-04927-8_48","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:09:28Z","timestamp":1758388168000},"page":"502-511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PolarDETR: Enhancing Interpretability in\u00a0Multi-modal Methods for\u00a0Jawbone Lesion Detection in\u00a0CBCT"],"prefix":"10.1007","author":[{"given":"Yuxuan","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruohan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jupeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"48_CR1","doi-asserted-by":"crossref","unstructured":"Bannur, S., et\u00a0al.: Learning to exploit temporal structure for biomedical vision-language processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15016\u201315027 (2023)","DOI":"10.1109\/CVPR52729.2023.01442"},{"key":"48_CR2","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229 (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"issue":"10","key":"48_CR3","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1016\/j.joen.2024.07.012","volume":"50","author":"RQ Chen","year":"2024","unstructured":"Chen, R.Q., et al.: Leveraging pretrained transformers for efficient segmentation and lesion detection in cone-beam computed tomography scans. J. Endodontics 50(10), 1505\u20131514 (2024)","journal-title":"J. Endodontics"},{"key":"48_CR4","doi-asserted-by":"crossref","unstructured":"Cho, H.: CNN-based autoencoder and post-training quantization for on-device anomaly detection of cartesian coordinate robots. In: 2024 IEEE 14th Annual Computing and Communication Workshop and Conference, pp. 0662\u20130666 (2024)","DOI":"10.1109\/CCWC60891.2024.10427937"},{"issue":"3","key":"48_CR5","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.ymeth.2013.05.004","volume":"61","author":"CM Colangelo","year":"2013","unstructured":"Colangelo, C.M., Chung, L., Bruce, C., Cheung, K.H.: Review of software tools for design and analysis of large scale MRM proteomic datasets. Methods 61(3), 287\u2013298 (2013)","journal-title":"Methods"},{"key":"48_CR6","first-page":"74461","volume":"37","author":"J Cui","year":"2024","unstructured":"Cui, J., Tian, Z., Zhong, Z., Qi, X., Yu, B., Zhang, H.: Decoupled kullback-leibler divergence loss. Adv. Neural. Inf. Process. Syst. 37, 74461\u201374486 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"48_CR7","doi-asserted-by":"crossref","unstructured":"Hossain, E., Sharif, O., Hoque, M.M., Preum, S.M.: Align before attend: aligning visual and textual features for multimodal hateful content detection (2024). arXiv preprint arXiv:2402.09738","DOI":"10.18653\/v1\/2024.eacl-srw.12"},{"key":"48_CR8","doi-asserted-by":"crossref","unstructured":"Huang, S.C., Shen, L., Lungren, M.P., Yeung, S.: Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3942\u20133951 (2021)","DOI":"10.1109\/ICCV48922.2021.00391"},{"key":"48_CR9","unstructured":"Jaeger, P.F., et al.: Retina u-net: Embarrassingly simple exploitation of segmentation supervision for medical object detection. In: Machine Learning for Health Workshop, pp. 171\u2013183 (2020)"},{"issue":"9","key":"48_CR10","doi-asserted-by":"publisher","first-page":"939","DOI":"10.3390\/diagnostics14090939","volume":"14","author":"A Lastrucci","year":"2024","unstructured":"Lastrucci, A., Wandael, Y., Ricci, R., Maccioni, G., Giansanti, D.: The integration of deep learning in radiotherapy: exploring challenges, opportunities, and future directions through an umbrella review. Diagnostics 14(9), 939 (2024)","journal-title":"Diagnostics"},{"issue":"1","key":"48_CR11","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","volume":"34","author":"J Li","year":"2020","unstructured":"Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. 34(1), 50\u201370 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"48_CR12","doi-asserted-by":"crossref","unstructured":"Liu, M., Liu, Y., Cui, H., Li, C., Ma, J.: Mgct: Mutual-guided cross-modality transformer for survival outcome prediction using integrative histopathology-genomic features. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine, pp. 1306\u20131312 (2023)","DOI":"10.1109\/BIBM58861.2023.10385897"},{"key":"48_CR13","first-page":"1","volume":"61","author":"S Mei","year":"2023","unstructured":"Mei, S., Jiang, R., Ma, M., Song, C.: Rotation-invariant feature learning via convolutional neural network with cyclic polar coordinates convolutional layer. IEEE Trans. Geosci. Remote Sens. 61, 1\u201313 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"1","key":"48_CR14","first-page":"8897275","volume":"2021","author":"A Pel\u00e9","year":"2021","unstructured":"Pel\u00e9, A., Berry, P.A., Evanno, C., Jordana, F.: Evaluation of mental foramen with cone beam computed tomography: a systematic review of literature. Radiol. Res. Pract. 2021(1), 8897275 (2021)","journal-title":"Radiol. Res. Pract."},{"key":"48_CR15","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763 (2021)"},{"key":"48_CR16","volume":"26","author":"T Shah","year":"2018","unstructured":"Shah, T.: Measuring object detection models\u2013map\u2013what is mean average precision. Tarang Shah-Blog 26, 104332 (2018)","journal-title":"Tarang Shah-Blog"},{"issue":"5","key":"48_CR17","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1093\/dmfr\/twae022","volume":"53","author":"YJ Shi","year":"2024","unstructured":"Shi, Y.J., et al.: Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress. Dentomaxillofacial Radiol. 53(5), 271\u2013280 (2024)","journal-title":"Dentomaxillofacial Radiol."},{"key":"48_CR18","doi-asserted-by":"crossref","unstructured":"Varghese, R., Sambath, M.: Yolov8: A novel object detection algorithm with enhanced performance and robustness. In: 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, pp.\u00a01\u20136 (2024)","DOI":"10.1109\/ADICS58448.2024.10533619"},{"issue":"11","key":"48_CR19","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.3390\/bioengineering10111267","volume":"10","author":"X Wang","year":"2023","unstructured":"Wang, X., Li, X., Du, R., Zhong, Y., Lu, Y., Song, T.: Anatomical prior-based automatic segmentation for cardiac substructures from computed tomography images. Bioengineering 10(11), 1267 (2023)","journal-title":"Bioengineering"},{"key":"48_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wu, Z., Agarwal, D., Sun, J.: Medclip: Contrastive learning from unpaired medical images and text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing. vol.\u00a02022, p.\u00a03876 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.256"},{"key":"48_CR21","doi-asserted-by":"crossref","unstructured":"Wu, X., Zhu, F., Zhao, R., Li, H.: Cora: Adapting clip for open-vocabulary detection with region prompting and anchor pre-matching. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7031\u20137040 (2023)","DOI":"10.1109\/CVPR52729.2023.00679"},{"key":"48_CR22","unstructured":"Xie, Y., Yang, B., Guan, Q., Zhang, J., Wu, Q., Xia, Y.: Attention mechanisms in medical image segmentation: A survey (2023). arXiv preprint arXiv:2305.17937"},{"key":"48_CR23","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: An advanced object detection network. In: Proceedings of the 24th ACM International Conference on Multimedia, pp. 516\u2013520 (2016)","DOI":"10.1145\/2964284.2967274"},{"key":"48_CR24","unstructured":"Zhang, Y., Jiang, H., Miura, Y., Manning, C.D., Langlotz, C.P.: Contrastive learning of medical visual representations from paired images and text. In: Machine Learning for Healthcare Conference, pp. 2\u201325 (2022)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04927-8_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:09:38Z","timestamp":1758388178000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04927-8_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049261","9783032049278"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04927-8_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}