An Efficient Transfer Learning With Prompt Learning for Brain Disorders Diagnosis

The limited availability of training data significantly restricts the performance of deep supervised models for brain disease diagnosis. It is crucial to develop a learning framework through cross-disease transfer learning that can extract more information from the limited data. To address this challenge, we concentrate on prompt learning and endeavor to extend its application to the brain networks. Specifically, we propose a novel prompt learning framework called BPformer, which integrates knowledge transferred across diseases via specific prompts while keeping the original architecture of BPformer unchanged. The specific prompts incorporate 1) a mask prompt to determine whether the edges are noisy or discriminating, 2) disorder prompts for modeling consistent and disorder-specific knowledge, and 3) adaptive instance-level prompts to account for inter-individual variations. We evaluate BPformer on the private center Nanjing Medical University dataset, the public Autism Brain Imaging Data Exchange dataset, and the public Alzheimer's Disease Neuroimaging Initiative dataset. We demonstrate the effectiveness of the proposed model across various disease classification tasks, including major depressive disorder, bipolar disorder, alzheimer's disease, and autism spectrum disorder diagnoses. In addition, the proposed method enables disease interpretability and subtype analysis, empowering physicians to provide patients with more accurate and fine-grained treatment plans.