{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:03:55Z","timestamp":1758845035323,"version":"3.44.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032055583","type":"print"},{"value":"9783032055590","type":"electronic"}],"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\/978-3-032-05559-0_3","type":"book-chapter","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:30:10Z","timestamp":1758767410000},"page":"21-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Training Set Diversity: A Key Factor in\u00a0AI-Driven Breast Ultrasound Classification"],"prefix":"10.1007","author":[{"given":"Rebecca","family":"Mes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Wijkhuizen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lennard M.","family":"van Karnenbeek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8111-1930","authenticated-orcid":false,"given":"Ritse","family":"Mann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9968-3723","authenticated-orcid":false,"given":"Theo","family":"Ruers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3977-6864","authenticated-orcid":false,"given":"Freija","family":"Geldof","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4443-1614","authenticated-orcid":false,"given":"Behdad","family":"Dashtbozorg","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"3_CR1","unstructured":"World Health Organization. Breast cancer\u2014who.int (2024). https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/breast-cancer. Accessed 05 Dec 2024"},{"issue":"17","key":"3_CR2","doi-asserted-by":"publisher","first-page":"3106","DOI":"10.1002\/cncr.28174","volume":"119","author":"S Hofvind","year":"2013","unstructured":"Hofvind, S., Ursin, G., Tretli, S., Sebu\u00f8deg\u00e5rd, S., M\u00f8ller, B.: Breast cancer mortality in participants of the Norwegian breast cancer screening program. Cancer 119(17), 3106\u20133112 (2013)","journal-title":"Cancer"},{"issue":"1","key":"3_CR3","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.patcog.2009.05.012","volume":"43","author":"H-D Cheng","year":"2010","unstructured":"Cheng, H.-D., Shan, J., Wen, J., Guo, Y., Zhang, L.: Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recogn. 43(1), 299\u2013317 (2010)","journal-title":"Pattern Recogn."},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"6528752","DOI":"10.1155\/int\/6528752","volume":"2024","author":"M Alruily","year":"2024","unstructured":"Alruily, M., Mahmoud, A.A., Allahem, H., Mostafa, A.M., Shabana, H., Ezz, M.: Enhancing breast cancer detection in ultrasound images: An innovative approach using progressive fine-tuning of vision transformer models. Int. J. Intell. Syst. 2024(1), 6528752 (2024)","journal-title":"Int. J. Intell. Syst."},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-019-0349-x","volume":"19","author":"Z Cao","year":"2019","unstructured":"Cao, Z., Duan, L., Yang, G., Yue, T., Chen, Q.: An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med. Imaging 19, 1\u20139 (2019)","journal-title":"BMC Med. Imaging"},{"issue":"13","key":"3_CR6","doi-asserted-by":"publisher","first-page":"2242","DOI":"10.3390\/diagnostics13132242","volume":"13","author":"MD Ali","year":"2023","unstructured":"Ali, M.D., et al.: Breast cancer classification through meta-learning ensemble technique using convolution neural networks. Diagnostics 13(13), 2242 (2023)","journal-title":"Diagnostics"},{"issue":"1","key":"3_CR7","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1186\/s41747-024-00480-y","volume":"8","author":"P Marco","year":"2024","unstructured":"Marco, P., Ricciardi, V., Montesano, M., Cassano, E., Origgi, D.: Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions? Eur. Radiol. Exp. 8(1), 121 (2024)","journal-title":"Eur. Radiol. Exp."},{"issue":"4","key":"3_CR8","first-page":"2954","volume":"33","author":"G Yang","year":"2023","unstructured":"Yang, G., et al.: Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study. Eur. Radiol. 33(4), 2954\u20132964 (2023)","journal-title":"Eur. Radiol."},{"issue":"10","key":"3_CR9","doi-asserted-by":"publisher","first-page":"1859","DOI":"10.3390\/diagnostics11101859","volume":"11","author":"EY Kalafi","year":"2021","unstructured":"Kalafi, E.Y., et al.: Classification of breast cancer lesions in ultrasound images by using attention layer and loss ensemble in deep convolutional neural networks. Diagnostics 11(10), 1859 (2021)","journal-title":"Diagnostics"},{"issue":"19","key":"3_CR10","doi-asserted-by":"publisher","first-page":"7714","DOI":"10.1088\/1361-6560\/aa82ec","volume":"62","author":"S Han","year":"2017","unstructured":"Han, S., et al.: A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys. Med. Biol. 62(19), 7714 (2017)","journal-title":"Phys. Med. Biol."},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"104407","DOI":"10.1016\/j.compbiomed.2021.104407","volume":"133","author":"Y Ero\u011flu","year":"2021","unstructured":"Ero\u011flu, Y., Yildirim, M., \u00c7inar, A.: Convolutional neural networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mrmr. Comput. Biol. Med. 133, 104407 (2021)","journal-title":"Comput. Biol. Med."},{"issue":"6","key":"3_CR12","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1109\/TMI.2023.3236011","volume":"42","author":"Y Mo","year":"2023","unstructured":"Mo, Y., et al.: Hover-trans: Anatomy-aware hover-transformer for ROI-free breast cancer diagnosis in ultrasound images. IEEE Trans. Med. Imaging 42(6), 1696\u20131706 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Gheflati, B., Rivaz, H.: Vision transformers for classification of breast ultrasound images. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 480\u2013483. IEEE (2022)","DOI":"10.1109\/EMBC48229.2022.9871809"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Yu, W., Wang, X.: MambaOut: do we really need mamba for vision? arXiv preprint arXiv:2405.07992 (2024)","DOI":"10.1109\/CVPR52734.2025.00423"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Nasiri-Sarvi, A., Hosseini, M.S., Rivaz, H.: Vision mamba for classification of breast ultrasound images. arXiv preprint arXiv:2407.03552 (2024)","DOI":"10.1007\/978-3-031-77789-9_15"},{"key":"3_CR16","unstructured":"American Cancer Society. Breast cancer facts & figures 2024-2025 (2024). Accessed 09 Dec 2024"},{"issue":"5","key":"3_CR17","doi-asserted-by":"publisher","first-page":"3595","DOI":"10.3390\/curroncol29050291","volume":"29","author":"PB Gordon","year":"2022","unstructured":"Gordon, P.B.: The impact of dense breasts on the stage of breast cancer at diagnosis: a review and options for supplemental screening. Curr. Oncol. 29(5), 3595\u20133636 (2022)","journal-title":"Curr. Oncol."},{"issue":"6","key":"3_CR18","doi-asserted-by":"publisher","first-page":"061108","DOI":"10.1117\/1.JMI.10.6.061108","volume":"10","author":"C Koo","year":"2023","unstructured":"Koo, C., et al.: Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation. J. Med. Imaging 10(6), 061108 (2023)","journal-title":"J. Med. Imaging"},{"key":"3_CR19","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)","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: MViTv2: improved multiscale vision transformers for classification and detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4804\u20134814 (2022)","DOI":"10.1109\/CVPR52688.2022.00476"},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"106438","DOI":"10.1016\/j.compbiomed.2022.106438","volume":"152","author":"AA Ardakani","year":"2023","unstructured":"Ardakani, A.A., Mohammadi, A., Mirza-Aghazadeh-Attari, M., Acharya, U.R.: An open-access breast lesion ultrasound image database: applicable in artificial intelligence studies. Comput. Biol. Med. 152, 106438 (2023)","journal-title":"Comput. Biol. Med."},{"key":"3_CR22","doi-asserted-by":"publisher","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"key":"3_CR23","doi-asserted-by":"publisher","first-page":"107292","DOI":"10.1016\/j.engappai.2023.107292","volume":"127","author":"A Iqbal","year":"2024","unstructured":"Iqbal, A., Sharif, M.: Memory-efficient transformer network with feature fusion for breast tumor segmentation and classification task. Eng. Appl. Artif. Intell. 127, 107292 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-031-16437-8_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","author":"Z Lin","year":"2022","unstructured":"Lin, Z., Lin, J., Zhu, L., Fu, H., Qin, J., Wang, L.: A new dataset and a baseline model for breast lesion detection in ultrasound videos. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13433, pp. 614\u2013623. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_59"},{"issue":"4","key":"3_CR25","doi-asserted-by":"publisher","first-page":"3110","DOI":"10.1002\/mp.16812","volume":"51","author":"W G\u00f3mez-Flores","year":"2024","unstructured":"G\u00f3mez-Flores, W., Gregorio-Calas, M.J., Albuquerque Pereira, W.C.: BUS-BRA: a breast ultrasound dataset for assessing computer-aided diagnosis systems. Med. Phys. 51(4), 3110\u20133123 (2024)","journal-title":"Med. Phys."},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Vallez, N., Bueno, G., Deniz, O., Rienda, M.A., Pastor, C.: BUS-UCLM: breast ultrasound lesion segmentation dataset (2024)","DOI":"10.1038\/s41597-025-04562-3"},{"key":"3_CR27","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.patcog.2018.01.032","volume":"79","author":"A Rodtook","year":"2018","unstructured":"Rodtook, A., Kirimasthong, K., Lohitvisate, W., Makhanov, S.S.: Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities. Pattern Recogn. 79, 172\u2013182 (2018)","journal-title":"Pattern Recogn."},{"issue":"4","key":"3_CR28","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","volume":"22","author":"MH Yap","year":"2017","unstructured":"Yap, M.H., et al.: Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J. Biomed. Health Inform. 22(4), 1218\u20131226 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"3_CR29","unstructured":"Geertsma, T.: Ultrasound cases, 5 breast and axilla. https:\/\/www.ultrasoundcases.info\/cases\/breast-and-axilla\/. Accessed 29 Jan 2025"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05559-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T02:30:19Z","timestamp":1758767419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05559-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032055583","9783032055590"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05559-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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":"Deep-Breath","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care","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":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"deep-breath2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/deep-breath-miccai.github.io\/deepbreath-2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}