50 citations · 94 across the 5 of their papers we have counts for
5 papers
Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning
Xiaoyang Chen, Jinjian Wu, Wenjiao Lyu +6
Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface re…
Learning MRI Artifact Removal With Unpaired Data
Siyuan Liu, Kim-Han Thung, Liangqiong Qu +3
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine learning driven techniques for RAC are predominantly ba…
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
Yinghuan Shi, Wanqi Yang, Kim-Han Thung +5
In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD pat…
Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging
Khoi Minh Huynh, Tiantian Xu, Ye Wu +7
During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, involving differentiation of neuronal types, dendritic arborization, axonal ingro…
Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks
Siyuan Liu, Kim-Han Thung, Weili Lin +2
In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural…