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Unsupervised deep learning architectures for anomaly detection in brain MRI scans

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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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Reproducció del document publicat a: https://doi.org/
10.3233/FAIA240415

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Capítol del llibre: Alsinet, Teresa,  Vilasís, Xavier , García, Daniel, Álvarez, Elena (eds.), Proceedings of the 26th International Conference of the Catalan Association for Artificial Intelligence, IOS Press, 2024, [ISBN 9781643685434], pp. 90-93

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Llibres / Capítols de llibre (Biologia Evolutiva, Ecologia i Ciències Ambientals)

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Unsupervised deep learning architectures for anomaly detection in brain MRI scans