Lei You

AC-Diff improves liver MRI harmonization by separating anatomy from contrast, achieving better segmentation results.

This paper introduces a two-stage framework combining 3D UNet and 3D GCN for accurate Couinaud segmentation in tumor-affected livers, achieving a mean Dice score of 0.828 on blind testing.

This study compares deep learning methods to predict cognitive decline using brain MRI and clinical data over two years, showing the value of integrating image and tabular data.

This study uses deep learning to derive image phenotypes from cardiac MRI and performs GWAS, identifying 323 SNPs and 628 genes associated with cardiac traits and diseases.

scIGANs uses GANs to impute dropouts in scRNA-seq data, improving downstream analysis and performance across datasets.