Comparing MedSigLIP and Structured Connectivity Models for ADHD and Schizophrenia Classification - Silva Neto, Eronides F. da; Bonafini, Beatriz L.; Bispo, Breno C.; Lima, Juliano B. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
The diagnosis of ADHD and schizophrenia is evolving with the integration of functional magnetic resonance imaging and machine learning. This study compares structured connectivity models (graph and hypergraph) with MedSigLIP foundation-model embeddings for disorder classification. Subject-level representations were extracted from the ADHD-200 and COBRE datasets and evaluated using 5-fold cross-validation. Among four classifiers, SVM consistently achieved the best performance. For ADHD, SVM reached an mAUC of 64.89%, approaching a graph-based baseline. For schizophrenia, it achieved an mAUC of 62.48%. These results indicate a competitive alternative to handcrafted connectivity features, even without disorder-specific pretraining.