Federated disentangled representation learning for unsupervised brain anomaly detection

Cosmin I. Bercea; Benedikt Wiestler; Daniel Rueckert; Shadi Albarqouni

doi:10.1038/s42256-022-00515-2

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Federated disentangled representation learning for unsupervised brain anomaly detection

Cosmin I. Bercea Benedikt Wiestler Daniel Rueckert Shadi Albarqouni

Year: 2022 Journal: Nature Machine Intelligence Vol: 4 (8)Pages: 685-695 Publisher: Nature Portfolio

DOI: 10.1038/s42256-022-00515-2

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Keywords:

Computer science Artificial intelligence Anomaly detection Deep learning Segmentation Autoencoder Convolutional neural network Workflow Feature learning Machine learning Pattern recognition (psychology) Database

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COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Brain Tumor Detection and Classification

Life Sciences → Neuroscience → Neurology

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Federated disentangled representation learning for unsupervised brain anomaly detection

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