# A Bi-Pyramid Multimodal Fusion Method for the Diagnosis of Bipolar Disorders

arXiv: https://arxiv.org/abs/2401.07571 · HTML: https://mlsanity.com/paper/2401.07571/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation](https://mlsanity.com/paper/2206.02425/) (2022)
- [NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation](https://mlsanity.com/paper/2208.14876/) (2022)
- [Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs](https://mlsanity.com/paper/2207.02094/) (2022)
- [New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning](https://mlsanity.com/paper/2211.07374/) (2022)

## Built on by (most cited first, 1 of 1)

- [Improving Multimodal Brain Encoding Model with Dynamic Subject-awareness Routing](https://mlsanity.com/paper/2510.04670/) (2025)

## Abstract (arXiv)

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.