{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T04:19:56Z","timestamp":1727756396156},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685434","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"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":[[2024,9,25]]},"abstract":"<jats:p>Brain imaging techniques, particularly magnetic resonance imaging (MRI), play a crucial role in understanding the neurocognitive phenotype and associated challenges of many neurological disorders, providing detailed insights into the structural alterations in the brain. Despite advancements, the links between cognitive performance and brain anatomy remain unclear. The complexity of analyzing brain MRI scans requires expertise and time, prompting the exploration of artificial intelligence for automated assistance. In this context, unsupervised deep learning techniques, particularly Transformers and Autoencoders, offer a solution by learning the distribution of healthy brain anatomy and detecting alterations in unseen scans. In this work, we evaluate several unsupervised models to reconstruct healthy brain scans and detect synthetic anomalies.<\/jats:p>","DOI":"10.3233\/faia240415","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:48:01Z","timestamp":1727689681000},"source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans"],"prefix":"10.3233","author":[{"given":"Jordi","family":"Mal\u00e9","sequence":"first","affiliation":[{"name":"HER - Human-Environment Research Group, La Salle - URL, Barcelona, Spain"}]},{"given":"V\u00edctor","family":"Xirau","sequence":"additional","affiliation":[{"name":"HER - Human-Environment Research Group, La Salle - URL, Barcelona, Spain"}]},{"given":"Juan","family":"Fortea","sequence":"additional","affiliation":[{"name":"Sant Pau Memory Unit, Hospital de Sant Pau i la Santa Creu, Barcelona, Spain"}]},{"given":"Yann","family":"Heuz\u00e9","sequence":"additional","affiliation":[{"name":"Univ. Bordeaux, CNRS, Minist\u00e8re de la Culture, PACEA, UMR 5199, Pessac, France"}]},{"given":"Neus","family":"Mart\u00ednez-Abad\u00edas","sequence":"additional","affiliation":[{"name":"Departament de Biologia Evolutiva, Ecologia i Ci\u00e8ncies Ambientals (BEECA), Facultat de Biologia, Universitat de Barcelona (UB), Barcelona, Spain"}]},{"given":"Xavier","family":"Sevillano","sequence":"additional","affiliation":[{"name":"HER - Human-Environment Research Group, La Salle - URL, Barcelona, Spain"}]}],"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\/FAIA240415","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:48:01Z","timestamp":1727689681000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"ISBN":["9781643685434"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240415","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}