Incomplete multi-modal brain image fusion for epilepsy classification

Multi-modal brain imaging data reflect brain structural and functional information from different aspects, which have been widely used in brain disease diagnosis, including epilepsy and Alzheimer's disease. In practice, it is difficult to obtain all the modalities of each subject due to high cost or equipment limitation. Therefore, it is highly essential to fuse incomplete multi-modality data to improve the diagnostic accuracy. The traditional methods need to perform data cleansing and discard incomplete subjects from the data, which leads to inefficient training and poor robustness. For addressing this problem, this paper proposes an incomplete multi-modality data fusion method based on low-rank representation for the diagnosis of epilepsy and its subtypes. Specifically, we designed an objective function that simultaneously learns the low-rank representation of the complete modality part, and recovers the incomplete modality by the correlation between different modalities. The proposed model can be optimized by using alternating direction method of multipliers. Extensive evaluation of the proposed method on epilepsy classification task with incomplete DTI and fMRI data showed that our method can achieve promising classification results in identifying epilepsy and its subtypes.