Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping
CoRR(2023)
摘要
The detection of heterogeneous mental disorders based on brain readouts
remains challenging due to the complexity of symptoms and the absence of
reliable biomarkers. This paper introduces CAM (Cortical Anomaly Detection
through Masked Image Modeling), a novel self-supervised framework designed for
the unsupervised detection of complex brain disorders using cortical surface
features. We employ this framework for the detection of individuals on the
psychotic spectrum and demonstrate its capabilities compared to
state-of-the-art methods, achieving an AUC of 0.696 for Schizoaffective and
0.769 for Schizophreniform, without the need for any labels. Furthermore, the
analysis of atypical cortical regions, including Pars Triangularis and several
frontal areas often implicated in schizophrenia, provides further confidence in
our approach. Altogether, we demonstrate a scalable approach for anomaly
detection of complex brain disorders based on cortical abnormalities. The code
will be made available at https://github.com/chadHGY/CAM.
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