4 citations · 9 across the 9 of their papers we have counts for
10 papers
Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
Juanhua Zhang, Ruodan Yan, Alessandro Perelli +2
Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers th…
CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis
Lan Jiang, Ye Mao, Xi Chen +2
MRI synthesis promises to mitigate the challenge of missing MRI modality in clinical practice. Diffusion model has emerged as an effective technique for image synthesis by modellin…
DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution
Ye Mao, Lan Jiang, Xi Chen +1
Multi-contrast magnetic resonance imaging (MRI) is the most common management tool used to characterize neurological disorders based on brain tissue contrasts. However, acquiring h…
Multi-modal learning for predicting the genotype of glioma
Yiran Wei, Xi Chen, Lei Zhu +4
The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrati…
Collaborative learning of images and geometrics for predicting isocitrate dehydrogenase status of glioma
Yiran Wei, Chao Li, Xi Chen +2
The isocitrate dehydrogenase (IDH) gene mutation status is an important biomarker for glioma patients. The gold standard of IDH mutation detection requires tumour tissue obtained v…
Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks
Yiran Wei, Yonghao Li, Xi Chen +3
Glioma is a common malignant brain tumor with distinct survival among patients. The isocitrate dehydrogenase (IDH) gene mutation provides critical diagnostic and prognostic value f…
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
Yifan Li, Chao Li, Yiran Wei +3
Glioblastoma is profoundly heterogeneous in regional microstructure and vasculature. Characterizing the spatial heterogeneity of glioblastoma could lead to more precise treatment.…
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification
Chao Li, Yiran Wei, Xi Chen +1
Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a…
Expectation-Maximization Regularized Deep Learning for Weakly Supervised Tumor Segmentation for Glioblastoma
Chao Li, Wenjian Huang, Xi Chen +3
We present an Expectation-Maximization (EM) Regularized Deep Learning (EMReDL) model for weakly supervised tumor segmentation. The proposed framework is tailored to glioblastoma, a…
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
Yifan Li, Chao Li, Stephen Price +2
Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor…