{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T13:12:54Z","timestamp":1666012374462},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"abstract":"<jats:p>Breast cancer must be detected early to reduce the mortality rate. Ultrasound images can make it easier for the clinician to diagnose cases of dense breasts. This study presents a deep vision transformer-based approach for predicting breast cancer malignancy scores from ultrasound images. In particular, various state-of-the-art deep vision transformers such as BEiT, CaiT, Swin, XCiT, and Vis-Former are adapted and trained to extract robust radiomics to classify breast tumors in ultrasound images as benign or malignant. The best-performing model is used to predict the malignancy score of each input ultrasound image. Experimental results revealed that the proposed approach achieves promising results for the detection of malignant tumors of the breast on ultrasound images.<\/jats:p>","DOI":"10.3233\/faia220351","type":"book-chapter","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:34:31Z","timestamp":1666010071000},"source":"Crossref","is-referenced-by-count":0,"title":["Transformer-Based Radiomics for Predicting Breast Tumor Malignancy Score in Ultrasonography"],"prefix":"10.3233","author":[{"given":"Mohamed A.","family":"Hassanien","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain"}]},{"given":"Vivek","family":"Kumar Singh","sequence":"additional","affiliation":[{"name":"Queen\u2019s University Belfast, United Kingdom"}]},{"given":"Domenec","family":"Puig","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain"}]},{"given":"Mohamed","family":"Abdel-Nasser","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, 43007 Tarragona, Spain"},{"name":"Electrical Engineering Department; Aswan University, Aswan, Egypt"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220351","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T12:34:33Z","timestamp":1666010073000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220351","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,17]]}}}