CITL uses transfer learning with pseudo-labelling and comorbidity mechanisms in an encoder-decoder setup to reach 76.32% accuracy on ASD and 73.15% on ADHD from fMRI, outperforming prior transfer learning baselines.
Brainsck: Brain structure and cognition alignment via knowledge injection and reactivation for diagnosing brain disorders
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Comorbidity-Informed Transfer Learning for Neuro-developmental Disorder Diagnosis
CITL uses transfer learning with pseudo-labelling and comorbidity mechanisms in an encoder-decoder setup to reach 76.32% accuracy on ASD and 73.15% on ADHD from fMRI, outperforming prior transfer learning baselines.