{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:51:52Z","timestamp":1767340312764,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031720857"},{"type":"electronic","value":"9783031720864"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-72086-4_68","type":"book-chapter","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:34:45Z","timestamp":1727987685000},"page":"724-733","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards Multi-modality Fusion and\u00a0Prototype-Based Feature Refinement for\u00a0Clinically Significant Prostate Cancer Classification in\u00a0Transrectal Ultrasound"],"prefix":"10.1007","author":[{"given":"Hong","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongsheng","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuming","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuebin","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"68_CR1","doi-asserted-by":"publisher","first-page":"3280","DOI":"10.1007\/s00464-013-2906-7","volume":"27","author":"S Ahmad","year":"2013","unstructured":"Ahmad, S., Cao, R., Varghese, T., Bidaut, L., Nabi, G.: Transrectal quantitative shear wave elastography in the detection and characterisation of prostate cancer. Surgical endoscopy 27, 3280\u20133287 (2013)","journal-title":"Surgical endoscopy"},{"issue":"4","key":"68_CR2","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1002\/pros.22925","volume":"75","author":"F Albright","year":"2015","unstructured":"Albright, F., Stephenson, R.A., Agarwal, N., Teerlink, C.C., Lowrance, W.T., Farnham, J.M., Albright, L.A.C.: Prostate cancer risk prediction based on complete prostate cancer family history. The Prostate 75(4), 390\u2013398 (2015)","journal-title":"The Prostate"},{"key":"68_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar, A., Modzelewski, R., Li, H., Ruan, S.: Multi-task deep learning based ct imaging analysis for covid-19 pneumonia: Classification and segmentation. Computers in biology and medicine 126, 104037 (2020)","journal-title":"Computers in biology and medicine"},{"doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. pp. 424\u2013432. Springer (2016)","key":"68_CR4","DOI":"10.1007\/978-3-319-46723-8_49"},{"unstructured":"Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: BMVC. vol.\u00a03 (2018)","key":"68_CR5"},{"issue":"3","key":"68_CR6","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/S1470-2045(22)00016-X","volume":"23","author":"AD Grey","year":"2022","unstructured":"Grey, A.D., Scott, R., Shah, B., Acher, P., Liyanage, S., Pavlou, M., Omar, R., Chinegwundoh, F., Patki, P., Shah, T.T., et\u00a0al.: Multiparametric ultrasound versus multiparametric mri to diagnose prostate cancer (cadmus): a prospective, multicentre, paired-cohort, confirmatory study. The Lancet Oncology 23(3), 428\u2013438 (2022)","journal-title":"The Lancet Oncology"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778 (2016)","key":"68_CR7","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132\u20137141 (2018)","key":"68_CR8","DOI":"10.1109\/CVPR.2018.00745"},{"key":"68_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119962","volume":"224","author":"R Huang","year":"2023","unstructured":"Huang, R., Xu, Z., Xie, Y., Wu, H., et\u00a0al.: Joint-phase attention network for breast cancer segmentation in DCE-MRI. Expert Systems with Applications 224, 119962 (2023)","journal-title":"Expert Systems with Applications"},{"key":"68_CR10","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.610785","volume":"11","author":"L Liang","year":"2021","unstructured":"Liang, L., Zhi, X., Sun, Y., Li, H., Wang, J., Xu, J., Guo, J.: A nomogram based on a multiparametric ultrasound radiomics model for discrimination between malignant and benign prostate lesions. Frontiers in Oncology 11, 610785 (2021)","journal-title":"Frontiers in Oncology"},{"unstructured":"Liu, J., Qin, Y.: Prototype refinement network for few-shot segmentation. arXiv preprint arXiv:2002.03579 (2020)","key":"68_CR11"},{"issue":"6","key":"68_CR12","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.eururo.2013.05.049","volume":"64","author":"S Loeb","year":"2013","unstructured":"Loeb, S., Vellekoop, A., Ahmed, H.U., Catto, J., Emberton, M., Nam, R., Rosario, D.J., Scattoni, V., Lotan, Y.: Systematic review of complications of prostate biopsy. European urology 64(6), 876\u2013892 (2013)","journal-title":"European urology"},{"issue":"1","key":"68_CR13","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1111\/his.13712","volume":"74","author":"A Matoso","year":"2019","unstructured":"Matoso, A., Epstein, J.I.: Defining clinically significant prostate cancer on the basis of pathological findings. Histopathology 74(1), 135\u2013145 (2019)","journal-title":"Histopathology"},{"doi-asserted-by":"crossref","unstructured":"Schoots, I.G., Roobol, M.J., Nieboer, D., Bangma, C.H., Steyerberg, E.W., Hunink, M.M.: Magnetic resonance imaging\u2013targeted biopsy may enhance the diagnostic accuracy of significant prostate cancer detection compared to standard transrectal ultrasound-guided biopsy: a systematic review and meta-analysis. European urology 68(3), 438\u2013450 (2015)","key":"68_CR14","DOI":"10.1016\/j.eururo.2014.11.037"},{"doi-asserted-by":"crossref","unstructured":"Selvaraju, R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 618\u2013626 (2017)","key":"68_CR15","DOI":"10.1109\/ICCV.2017.74"},{"doi-asserted-by":"crossref","unstructured":"Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A.: Cancer statistics, 2023. CA: A Cancer Journal for Clinicians 73(1), 17\u201348 (2023)","key":"68_CR16","DOI":"10.3322\/caac.21763"},{"doi-asserted-by":"crossref","unstructured":"Sun, Y.K., Zhou, B.Y., Miao, Y., Shi, Y.L., Xu, S.H., Wu, D.M., Zhang, L., Xu, G., Wu, T.F., Wang, L.F., et\u00a0al.: Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study. Eclinicalmedicine 60 (2023)","key":"68_CR17","DOI":"10.1016\/j.eclinm.2023.102027"},{"doi-asserted-by":"crossref","unstructured":"Wang, Z., Hong, Y.: A2FSeg: Adaptive multi-modal fusion network for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 673\u2013681. Springer (2023)","key":"68_CR18","DOI":"10.1007\/978-3-031-43901-8_64"},{"key":"68_CR19","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1007\/s00330-019-06436-w","volume":"30","author":"RR Wildeboer","year":"2020","unstructured":"Wildeboer, R.R., Mannaerts, C.K., van Sloun, R.J., Bud\u00e4us, L., Tilki, D., Wijkstra, H., Salomon, G., Mischi, M.: Automated multiparametric localization of prostate cancer based on b-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics. European radiology 30, 806\u2013815 (2020)","journal-title":"European radiology"},{"doi-asserted-by":"crossref","unstructured":"Wu, K., Du, B., Luo, M., Wen, H., Shen, Y., Feng, J.: Weakly supervised brain lesion segmentation via attentional representation learning. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part III 22. pp. 211\u2013219. Springer (2019)","key":"68_CR20","DOI":"10.1007\/978-3-030-32248-9_24"},{"issue":"2","key":"68_CR21","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.ultrasmedbio.2013.09.032","volume":"40","author":"Y Xiao","year":"2014","unstructured":"Xiao, Y., Zeng, J., Niu, L., Zeng, Q., Wu, T., Wang, C., Zheng, R., Zheng, H.: Computer-aided diagnosis based on quantitative elastographic features with supersonic shear wave imaging. Ultrasound in medicine & biology 40(2), 275\u2013286 (2014)","journal-title":"Ultrasound in medicine & biology"},{"key":"68_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101918","volume":"70","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Chen, H., Li, Y., Liu, Q., Xu, X., Wang, S., Yap, P.T., Shen, D.: Multi-task learning for segmentation and classification of tumors in 3d automated breast ultrasound images. Medical Image Analysis 70, 101918 (2021)","journal-title":"Medical Image Analysis"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72086-4_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T20:44:15Z","timestamp":1727988255000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72086-4_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720857","9783031720864"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72086-4_68","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"4 October 2024","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}