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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/219443
Unsupervised deep learning architectures for anomaly detection in brain MRI scans
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Abstract
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.
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MALÉ, Jordi, et al. Unsupervised deep learning architectures for anomaly detection in brain MRI scans. Capítol del llibre: Alsinet. Teresa. Vol. Vilasís, num. Xavier, pags. García. [consulted: 8 of August of 2026]. Available at: https://hdl.handle.net/2445/219443