{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:22:49Z","timestamp":1743049369094,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439865"},{"type":"electronic","value":"9783031439872"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43987-2_17","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"169-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Thyroid Nodule Diagnosis in\u00a0Dynamic Contrast-Enhanced Ultrasound via\u00a0Microvessel Infiltration Awareness"],"prefix":"10.1007","author":[{"given":"Haojie","family":"Han","sequence":"first","affiliation":[]},{"given":"Hongen","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Daoqiang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wentao","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Fang","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"21","key":"17_CR1","doi-asserted-by":"publisher","first-page":"5469","DOI":"10.3390\/cancers13215469","volume":"13","author":"M Radzina","year":"2021","unstructured":"Radzina, M., Ratniece, M., Putrins, D.S., et al.: Performance of contrast-enhanced ultrasound in thyroid nodules: review of current state and future perspectives. Cancers 13(21), 5469 (2021)","journal-title":"Cancers"},{"issue":"2","key":"17_CR2","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10396-020-01011-z","volume":"47","author":"Z Yongfeng","year":"2020","unstructured":"Yongfeng, Z., Ping, Z., Hong, P., Wengang, L., Yan, Z.: Superb microvascular imaging compared with contrast-enhanced ultrasound to assess microvessels in thyroid nodules. J. Med. Ultrasonics 47(2), 287\u2013297 (2020). https:\/\/doi.org\/10.1007\/s10396-020-01011-z","journal-title":"J. Med. Ultrasonics"},{"issue":"12","key":"17_CR3","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1016\/j.ultrasmedbio.2007.06.002","volume":"33","author":"YX Jiang","year":"2007","unstructured":"Jiang, Y.X., Liu, H., Liu, J.B., et al.: Breast tumor size assessment: comparison of conventional ultrasound and contrast-enhanced ultrasound. Ultrasound Med. Biol. 33(12), 1873\u20131881 (2007)","journal-title":"Ultrasound Med. Biol."},{"issue":"6","key":"17_CR4","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1109\/TMI.2021.3063421","volume":"40","author":"P Wan","year":"2021","unstructured":"Wan, P., Chen, F., Zhang, D., et al.: Hierarchical temporal attention network for thyroid nodule recognition using dynamic CEUS imaging. IEEE Trans. Med. Imaging 40(6), 1646\u20131660 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"17_CR5","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1109\/TMI.2021.3078370","volume":"40","author":"C Chen","year":"2021","unstructured":"Chen, C., Wang, Y., et al.: Domain knowledge powered deep learning for breast cancer diagnosis based on contrast-enhanced ultrasound videos. IEEE Trans. Med. Imaging 40(9), 2439\u20132451 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Manh, V. T., Zhou, J., Jia, X., Lin, Z., et al.: Multi-attribute attention network for interpretable diagnosis of thyroid nodules in ultrasound images. IEEE Trans. Ultrason. Ferroelect. Frequency Control 69(9), 2611\u20132620 (2022)","DOI":"10.1109\/TUFFC.2022.3190012"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.cmpb.2017.06.001","volume":"146","author":"WK Moon","year":"2017","unstructured":"Moon, W.K., Lee, Y.W., et al.: Computer-aided prediction of axillary lymph node status in breast cancer using tumor surrounding tissue features in ultrasound images. Comput. Meth. Programs Biomed. 146, 143\u2013150 (2017)","journal-title":"Comput. Meth. Programs Biomed."},{"key":"17_CR8","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-031-25066-8_16","volume-title":"ECCV 2022","author":"A Golts","year":"2023","unstructured":"Golts, A., Livneh, I., Zohar, Y., et al.: Simultaneous detection and classification of partially and weakly supervised cells. In: Karlinsky, L., et al. (eds.) ECCV 2022. LNCS, vol. 13803, pp. 313\u2013329. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_16"},{"key":"17_CR9","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/978-3-031-16440-8_23","volume-title":"MICCAI 2022","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Li, Z., Cui, X., Zhang, L., et al.: Key-frame guided network for thyroid nodule recognition using ultrasound videos. In: Wang, L., et al. (eds.) MICCAI 2022. LNCS, vol. 13434, pp. 238\u2013247. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16440-8_23"},{"issue":"9","key":"17_CR10","doi-asserted-by":"publisher","first-page":"2463","DOI":"10.1109\/TMI.2021.3079709","volume":"40","author":"X Wang","year":"2021","unstructured":"Wang, X., Jiang, L., Li, L., et al.: Joint learning of 3D lesion segmentation and classification for explainable COVID-19 diagnosis. IEEE Trans. Med. Imaging 40(9), 2463\u20132476 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"17_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-030-58548-8_7","volume-title":"Computer Vision \u2013 ECCV 2020","author":"H Wang","year":"2020","unstructured":"Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., Chen, L.-C.: Axial-DeepLab: stand-alone axial-attention for panoptic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12349, pp. 108\u2013126. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58548-8_7"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhang, Z., Qin, S., et al.: APAUNet: axis projection attention UNet for small target in 3D medical segmentation. In: Proceedings of the Asian Conference on Computer Vision, pp. 283\u2013298 (2022)","DOI":"10.1007\/978-3-031-26351-4_2"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, S., Zhu, X., Chen, L., Hou, J., et al.: Arbitrary shape text detection via segmentation with probability maps. IEEE Trans. Pattern Anal. Mach. Intell. 14 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3176122","DOI":"10.1109\/TPAMI.2022.3176122"},{"key":"17_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105173","volume":"185","author":"W G\u00f3mez-Flores","year":"2020","unstructured":"G\u00f3mez-Flores, W., et al.: Assessment of the invariance and discriminant power of morphological features under geometric transformations for breast tumor classification. Comput. Methods Programs Biomed. 185, 105173 (2020)","journal-title":"Comput. Methods Programs Biomed."},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Yang, Q., et al.: Inceptext: a new inception-text module with deformable psroipooling for multi-oriented scene text detection. In: IJCAI, pp. 1071\u20131077(2018)","DOI":"10.24963\/ijcai.2018\/149"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: IEEE Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"17_CR17","unstructured":"Chen, J., et al.: TransuNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"17_CR18","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":"17_CR19","doi-asserted-by":"crossref","unstructured":"Tran, D., et al.: Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., et al.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450\u20136459 (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., et al.: A closer look at spatiotemporal convolutions for action recognition . In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 6450\u20136459 (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Mutegeki, R., Han, D. S., et al.: A CNN-LSTM approach to human activity recognition. In: 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 362\u2013366 (2022)","DOI":"10.1109\/ICAIIC48513.2020.9065078"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43987-2_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T19:18:42Z","timestamp":1730229522000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"0","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":"32% - 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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}