activity
20202023
most citedBrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification

3 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV2023

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…

cs.LG2022★ 1 cited

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…

eess.IV2021★ 1 cited

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…

cs.LG2021

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.…

cs.LG2021★ 3 cited

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…

eess.IV2021

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…

eess.IV2020

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…