Abstract

The subjective nature of diagnosing mental disorders complicates achieving accurate diagnoses. The complex relationship among disorders further exacerbates this issue, particularly in clinical practice where conditions like bipolar disorder (BP) and schizophrenia (SZ) can present similar clinical symptoms and cognitive impairments. To address these challenges, this paper proposes a mutualistic multi-network noisy label learning (MMNNLL) method, which aims to enhance diagnostic accuracy by leveraging neuroimaging data under the presence of potential clinical diagnosis bias or errors. MMNNLL effectively utilizes multiple deep neural networks (DNNs) for learning from data with noisy labels by maximizing the consistency among DNNs in identifying and utilizing samples with clean and noisy labels. Experimental results on public CIFAR-10 and PathMNIST datasets demonstrate the effectiveness of our method in classifying independent test data across various types and levels of label noise. Additionally, our MMNNLL method significantly outperforms state-of-the-art noisy label learning methods. When applied to brain functional connectivity data from BP and SZ patients, our method identifies two biotypes that show more pronounced group differences, and improved classification accuracy compared to the original clinical categories, using both traditional machine learning and advanced deep learning techniques. In summary, our method effectively addresses the possible inaccuracy in nosology of mental disorders and achieves transdiagnostic classification through robust noisy label learning via multi-network collaboration and competition.