6 citations · 8 across the 4 of their papers we have counts for
7 papers
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…
Predicting conversion of mild cognitive impairment to Alzheimer's disease
Yiran Wei, Stephen J. Price, Carola-Bibiane Schönlieb +1
Alzheimer's disease (AD) is the most common age-related dementia. Mild cognitive impairment (MCI) is the early stage of cognitive decline before AD. It is crucial to predict the MC…
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.…
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…