260 citations · 289 across the 5 of their papers we have counts for
5 papers
Decoupling Disease, Covariates, and Individual Variability: A Unified Disentanglement Framework for Medical Image Classification
Shengjie Zhang, Jinglin Zhang, Zhuangzhuang Jiang +10
Accurately isolating disease-related features from confounding covariates (e.g., age, gender, site) and individual variations remains a fundamental challenge in medical image class…
Removing Motion Artifact in MRI by Using a Perceptual Loss Driven Deep Learning Framework
Ziheng Guo, Danqun Zheng, Shuai Li +8
Purpose: Deep learning-based MRI artifact correction methods often demonstrate poor generalization to clinical data. This limitation largely stems from the inability of deep learni…
HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity Synthesis of MR Images with Structure Preservation
Ziqi Yu, Botao Zhao, Shengjie Zhang +4
Synthesizing medical images while preserving their structural information is crucial in medical research. In such scenarios, the preservation of anatomical content becomes especial…
MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain
Ziqi Yu, Xiaoyang Han, Shengjie Zhang +3
Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding th…
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
Jun Ma, Yixin Wang, Xingle An +10
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based…