{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:20:27Z","timestamp":1743124827785,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031733758"},{"type":"electronic","value":"9783031733765"}],"license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-73376-5_15","type":"book-chapter","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T10:12:01Z","timestamp":1728382321000},"page":"154-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SelectiveKD: A Semi-supervised Framework for\u00a0Cancer Detection in\u00a0DBT Through Knowledge Distillation and\u00a0Pseudo-labeling"],"prefix":"10.1007","author":[{"given":"Laurent","family":"Dillard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyeonsoo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weonsuk","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tae Soo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Diba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thijs","family":"Kooi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Andharia, D., Shah, H., Prajapati, A.D., Bhansali, A.D., Shah, A., Desai, D.: Digital breast tomosynthesis(DBT) vs 2D mammography and impact of combined use: a meta-analysis. In: medRxiv (2023)","DOI":"10.1101\/2023.12.07.23299674"},{"issue":"1","key":"15_CR2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1148\/radiol.2017171148","volume":"287","author":"M Bahl","year":"2018","unstructured":"Bahl, M., Gaffney, S., McCarthy, A.M., Lowry, K.P., Dang, P.A., Lehman, C.D.: Breast cancer characteristics associated with 2d digital mammography versus digital breast tomosynthesis for screening-detected and interval cancers. Radiology 287(1), 49\u201357 (2018)","journal-title":"Radiology"},{"key":"15_CR3","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: MixMatch: a holistic approach to semi-supervised learning. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1109\/TPAMI.2022.3201576","volume":"46","author":"Y Chen","year":"2022","unstructured":"Chen, Y., Mancini, M., Zhu, X., Akata, Z.: Semi-supervised and unsupervised deep visual learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46, 1327\u20131347 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhu, X., Li, W., Gong, S.: Semi-supervised learning under class distribution mismatch. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a034, pp. 3569\u20133576 (2020)","DOI":"10.1609\/aaai.v34i04.5763"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"587","DOI":"10.2214\/AJR.20.23976","volume":"217","author":"SA Chikarmane","year":"2021","unstructured":"Chikarmane, S.A., Cochon, L.R., Khorasani, R., Sahu, S., Giess, C.S.: Screening mammography performance metrics of 2D digital mammography versus digital breast tomosynthesis in women with a personal history of breast cancer. Am. J. Roentgenol. 217, 587\u2013594 (2021)","journal-title":"Am. J. Roentgenol."},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837\u2013845 (1988)","journal-title":"Biometrics"},{"issue":"2","key":"15_CR8","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/s13193-021-01310-y","volume":"12","author":"E Dhamija","year":"2021","unstructured":"Dhamija, E., Gulati, M., Deo, S., Gogia, A., Hari, S.: Digital breast tomosynthesis: an overview. Indian J. Surg. Oncol. 12(2), 315\u2013329 (2021)","journal-title":"Indian J. Surg. Oncol."},{"issue":"1","key":"15_CR9","doi-asserted-by":"publisher","DOI":"10.1148\/rg.220060","volume":"43","author":"JE Goldberg","year":"2022","unstructured":"Goldberg, J.E., et al.: New horizons: artificial intelligence for digital breast tomosynthesis. Radiographics 43(1), e220060 (2022)","journal-title":"Radiographics"},{"key":"15_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015)"},{"key":"15_CR11","first-page":"1","volume":"61","author":"L Huang","year":"2023","unstructured":"Huang, L., Chen, Y., He, X.: Spectral-spatial masked transformer with supervised and contrastive learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1\u201318 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"15_CR12","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.3348\/kjr.2020.1227","volume":"22","author":"MJ Ko","year":"2021","unstructured":"Ko, M.J., et al.: Accuracy of digital breast tomosynthesis for detecting breast cancer in the diagnostic setting: a systematic review and meta-analysis. Korean J. Radiol. 22(8), 1240 (2021)","journal-title":"Korean J. Radiol."},{"key":"15_CR13","unstructured":"Lee, D.H., et\u00a0al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, Atlanta, vol.\u00a03, p.\u00a0896 (2013)"},{"issue":"3","key":"15_CR14","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.220159","volume":"5","author":"W Lee","year":"2023","unstructured":"Lee, W., Lee, H., Lee, H., Park, E.K., Nam, H., Kooi, T.: Transformer-based deep neural network for breast cancer classification on digital breast tomosynthesis images. Radiol. Artif. Intell. 5(3), e220159 (2023)","journal-title":"Radiol. Artif. Intell."},{"key":"15_CR15","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"issue":"9","key":"15_CR16","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/j.diii.2015.03.003","volume":"96","author":"T Nguyen","year":"2015","unstructured":"Nguyen, T., et al.: Overview of digital breast tomosynthesis: clinical cases, benefits, and disadvantages. Diagn. Interv. Imaging 96(9), 843\u2013859 (2015)","journal-title":"Diagn. Interv. Imaging"},{"key":"15_CR17","first-page":"996","volume":"2016","author":"CL Shahan","year":"2016","unstructured":"Shahan, C.L.: An overview of digital breast tomosynthesis. W. Va. Med. J. 2016, 996 (2016)","journal-title":"W. Va. Med. J."},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"9077","DOI":"10.1109\/JSTARS.2023.3319587","volume":"16","author":"Y Shen","year":"2023","unstructured":"Shen, Y., Shi, L., Zhao, J., Dong, Y., Wang, L.: Fully convolutional spectral-spatial fusion network integrating supervised contrastive learning for hyperspectral image classification. IEEE J. Sel. Topics Appl. Earth Obs. Rem. Sens. 16, 9077\u20139088 (2023)","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Rem. Sens."},{"issue":"1","key":"15_CR19","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1148\/radiol.211105","volume":"303","author":"Y Shoshan","year":"2022","unstructured":"Shoshan, Y., et al.: Artificial intelligence for reducing workload in breast cancer screening with digital breast tomosynthesis. Radiology 303(1), 69\u201377 (2022)","journal-title":"Radiology"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Singh, P., et al.: Shifting to machine supervision: annotation-efficient semi and self-supervised learning for automatic medical image segmentation and classification. arXiv preprint arXiv:2311.10319 (2023)","DOI":"10.1038\/s41598-024-61822-9"},{"key":"15_CR21","unstructured":"Tam\u00e9, I.d.A., Sirotkin, K., Carballeira, P., Escudero-Vi\u00f1olo, M.: Self-supervised curricular deep learning for chest X-ray image classification. arXiv preprint arXiv:2301.10687 (2023)"},{"key":"15_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-3-030-87234-2_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"M Tardy","year":"2021","unstructured":"Tardy, M., Mateus, D.: Trainable summarization to improve breast tomosynthesis classification. In: de Bruijne, M. (ed.) MICCAI 2021. LNCS, vol. 12907, pp. 140\u2013149. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_14"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imageNet classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10687\u201310698 (2020)","DOI":"10.1109\/CVPR42600.2020.01070"},{"issue":"5","key":"15_CR24","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1002\/int.22814","volume":"37","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Yang, J., Yu, J., Fan, J.: Semisupervised image classification by mutual learning of multiple self-supervised models. Int. J. Intell. Syst. 37(5), 3117\u20133141 (2022)","journal-title":"Int. J. Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Cancer Prevention, Detection, and Intervention"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73376-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T10:14:17Z","timestamp":1728382457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73376-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,9]]},"ISBN":["9783031733758","9783031733765"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73376-5_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,9]]},"assertion":[{"value":"9 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CaPTion","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Cancer Prevention through Early Detection","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":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caption2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/caption-workshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}