{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:22:03Z","timestamp":1768994523496,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556335","type":"print"},{"value":"9789819556342","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-5634-2_32","type":"book-chapter","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:24Z","timestamp":1768944204000},"page":"449-462","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ultra-Lightweight Thyroid Puncture Positioning Detection Guided by\u00a0Nodule Location"],"prefix":"10.1007","author":[{"given":"Shengqi","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yi","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Chengfan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Buyun","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Deng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"32_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107614","volume":"238","author":"H Bi","year":"2023","unstructured":"Bi, H., et al.: BPAT-Unet: boundary preserving assembled transformer Unet for ultrasound thyroid nodule segmentation. Comput. Methods Programs Biomed. 238, 107614 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"5","key":"32_CR2","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1210\/clinem\/dgz170","volume":"105","author":"M Castellana","year":"2020","unstructured":"Castellana, M., et al.: Performance of five ultrasound risk stratification systems in selecting thyroid nodules for FNA. J. Clin. Endocr. Metab. 105(5), 1659\u20131669 (2020)","journal-title":"J. Clin. Endocr. Metab."},{"issue":"4","key":"32_CR3","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1007\/s00330-023-10269-z","volume":"34","author":"C Chen","year":"2024","unstructured":"Chen, C., et al.: Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study. Eur. Radiol. 34(4), 2323\u20132333 (2024)","journal-title":"Eur. Radiol."},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Mobile-former: bridging mobilenet and transformer. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5270\u20135279 (2022)","DOI":"10.1109\/CVPR52688.2022.00520"},{"key":"32_CR5","unstructured":"Dosovitskiy, A.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"issue":"8","key":"32_CR6","doi-asserted-by":"publisher","first-page":"5932","DOI":"10.21037\/qims-23-1597","volume":"14","author":"H Du","year":"2024","unstructured":"Du, H., et al.: Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians. Quant. Imaging Med. Surg. 14(8), 5932 (2024)","journal-title":"Quant. Imaging Med. Surg."},{"key":"32_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106389","volume":"155","author":"H Gong","year":"2023","unstructured":"Gong, H., Chen, J., Chen, G., Li, H., Li, G., Chen, F.: Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput. Biol. Med. 155, 106389 (2023)","journal-title":"Comput. Biol. Med."},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Gulame, M.B., Dixit, V.V.: Hybrid deep learning assisted multi classification: grading of malignant thyroid nodules. Int. J. Numer. Methods Biomed. Eng. e3824 (2024)","DOI":"10.1002\/cnm.3824"},{"issue":"3","key":"32_CR9","doi-asserted-by":"publisher","first-page":"244","DOI":"10.36548\/jiip.2024.3.003","volume":"6","author":"DK Gummalla","year":"2024","unstructured":"Gummalla, D.K., Ganesan, S., Pokhrel, S., Somasiri, N., et al.: Enhanced early detection of thyroid abnormalities using a hybrid deep learning model: A sequential CNN and k-means clustering approach. J. Innov. Image Process. 6(3), 244\u2013261 (2024)","journal-title":"J. Innov. Image Process."},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"12","key":"32_CR11","doi-asserted-by":"publisher","first-page":"36039","DOI":"10.1007\/s11042-023-16605-1","volume":"83","author":"HA Helaly","year":"2024","unstructured":"Helaly, H.A., Badawy, M., Haikal, A.Y.: A review of deep learning approaches in clinical and healthcare systems based on medical image analysis. Multimed. Tools Appl. 83(12), 36039\u201336080 (2024)","journal-title":"Multimed. Tools Appl."},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Jiang, B., Wang, L., Xu, K., Moghekar, A., Kazanzides, P., Boctor, E.M.: Active needle tracking with wearable 2-DOF ultrasound scanner for lumbar puncture guidance. In: 2023 IEEE International Ultrasonics Symposium (IUS), pp.\u00a01\u20133. IEEE (2023)","DOI":"10.1109\/IUS51837.2023.10307139"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Jiang, Z., et al.: Needle segmentation using GAN: restoring thin instrument visibility in robotic ultrasound. IEEE Trans. Instrum. Meas. (2024)","DOI":"10.1109\/TIM.2024.3451569"},{"key":"32_CR14","doi-asserted-by":"publisher","first-page":"63482","DOI":"10.1109\/ACCESS.2020.2982390","volume":"8","author":"V Kumar","year":"2020","unstructured":"Kumar, V., et al.: Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8, 63482\u201363496 (2020)","journal-title":"IEEE Access"},{"key":"32_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127497","volume":"582","author":"G Li","year":"2024","unstructured":"Li, G., et al.: Fully automated diagnosis of thyroid nodule ultrasound using brain-inspired inference. Neurocomputing 582, 127497 (2024)","journal-title":"Neurocomputing"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"32_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1007\/978-3-030-01264-9_8","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Ma","year":"2018","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: ShuffleNet V2: practical guidelines for efficient CNN architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11218, pp. 122\u2013138. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_8"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Ma, X., Dai, X., Bai, Y., Wang, Y., Fu, Y.: Rewrite the stars. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5694\u20135703 (2024)","DOI":"10.1109\/CVPR52733.2024.00544"},{"issue":"9","key":"32_CR20","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1109\/TUFFC.2023.3255843","volume":"70","author":"N Masoumi","year":"2023","unstructured":"Masoumi, N., Rivaz, H., Hacihaliloglu, I., Ahmad, M.O., Reinertsen, I., Xiao, Y.: The big bang of deep learning in ultrasound-guided surgery: a review. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 70(9), 909\u2013919 (2023)","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"32_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1007\/978-3-030-01249-6_34","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Mehta","year":"2018","unstructured":"Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 561\u2013580. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01249-6_34"},{"issue":"19","key":"32_CR22","doi-asserted-by":"publisher","first-page":"3484","DOI":"10.3390\/math10193484","volume":"10","author":"DT Nguyen","year":"2022","unstructured":"Nguyen, D.T., Choi, J., Park, K.R.: Thyroid nodule segmentation in ultrasound image based on information fusion of suggestion and enhancement networks. Mathematics 10(19), 3484 (2022)","journal-title":"Mathematics"},{"key":"32_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.872601","volume":"16","author":"X Nie","year":"2022","unstructured":"Nie, X., et al.: N-net: a novel dense fully convolutional neural network for thyroid nodule segmentation. Front. Neurosci. 16, 872601 (2022)","journal-title":"Front. Neurosci."},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Pan, H., Zhou, Q., Latecki, L.J.: SGUnet: semantic guided Unet for thyroid nodule segmentation. In: IEEE International Symposium on Biomedical Imaging, pp. 630\u2013634. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434051"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Parsa, A.A., Gharib, H.: Thyroid nodule: current evaluation and management. In: The Thyroid and Its Diseases: A Comprehensive Guide for the Clinician, pp. 493\u2013516 (2019)","DOI":"10.1007\/978-3-319-72102-6_33"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobilenetV2: inverted residuals and linear bottlenecks. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"issue":"3","key":"32_CR27","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1109\/JBHI.2018.2852718","volume":"23","author":"W Song","year":"2018","unstructured":"Song, W., et al.: Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE J. Biom. Health Info. 23(3), 1215\u20131224 (2018)","journal-title":"IEEE J. Biom. Health Info."},{"key":"32_CR28","doi-asserted-by":"crossref","unstructured":"Sun, X., Wei, B., Jiang, Y., Mao, L., Zhao, Q.: Clip-TNSeg: a multi-modal hybrid framework for thyroid nodule segmentation in ultrasound images. IEEE Sig. Process. Lett. (2025)","DOI":"10.1109\/LSP.2025.3556789"},{"issue":"7","key":"32_CR29","doi-asserted-by":"publisher","first-page":"648","DOI":"10.3390\/bioengineering11070648","volume":"11","author":"S Vahdati","year":"2024","unstructured":"Vahdati, S., et al.: A multi-view deep learning model for thyroid nodules detection and characterization in ultrasound imaging. Bioengineering 11(7), 648 (2024)","journal-title":"Bioengineering"},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Vasu, P.K.A., Gabriel, J., Zhu, J., Tuzel, O., Ranjan, A.: Mobileone: an improved one millisecond mobile backbone. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7907\u20137917 (2023)","DOI":"10.1109\/CVPR52729.2023.00764"},{"key":"32_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108972","volume":"180","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Huang, H., Shao, Y., Chen, B.: DAC-net: a light-weight U-shaped network based efficient convolution and attention for thyroid nodule segmentation. Comput. Biol. Med. 180, 108972 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"32_CR32","doi-asserted-by":"publisher","first-page":"4807","DOI":"10.1038\/s41467-020-18497-3","volume":"11","author":"J Yu","year":"2020","unstructured":"Yu, J., et al.: Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics. Nat. Commun. 11(1), 4807 (2020)","journal-title":"Nat. Commun."},{"key":"32_CR33","unstructured":"Zhang, T., Li, L., Zhou, Y., Liu, W., Qian, C., Ji, X.: CAS-ViT: convolutional additive self-attention vision transformers for efficient mobile applications. arXiv preprint arXiv:2408.03703 (2024)"}],"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\/978-981-95-5634-2_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:27Z","timestamp":1768944207000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5634-2_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556335","9789819556342"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5634-2_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 January 2026","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":"Shanghai","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}