A Bi-Pyramid Multimodal Fusion Method for the Diagnosis of Bipolar Disorders
This work addresses the need for more accurate clinical diagnosis of bipolar disorders, though it is incremental as it builds on existing multimodal fusion strategies.
The paper tackled the problem of diagnosing bipolar disorders by proposing a novel multimodal fusion method using sMRI and fMRI data, achieving a balanced accuracy improvement from 0.657 to 0.732 on the OpenfMRI dataset.
Previous research on the diagnosis of Bipolar disorder has mainly focused on resting-state functional magnetic resonance imaging. However, their accuracy can not meet the requirements of clinical diagnosis. Efficient multimodal fusion strategies have great potential for applications in multimodal data and can further improve the performance of medical diagnosis models. In this work, we utilize both sMRI and fMRI data and propose a novel multimodal diagnosis model for bipolar disorder. The proposed Patch Pyramid Feature Extraction Module extracts sMRI features, and the spatio-temporal pyramid structure extracts the fMRI features. Finally, they are fused by a fusion module to output diagnosis results with a classifier. Extensive experiments show that our proposed method outperforms others in balanced accuracy from 0.657 to 0.732 on the OpenfMRI dataset, and achieves the state of the art.