activity
20202024
most citedSemantic Segmentation Using Deep Learning to Extract Total Extraocular Muscles and Optic Nerve from Orbital Computed Tomography Images

45 citations · 171 across the 20 of their papers we have counts for

collaborators

21 papers

physics.med-ph2024

A review on machine learning for arterial extraction and quantitative assessment on invasive coronary angiograms

Pukar Baral, Chen Zhao, Michele Esposito +1

Purpose of Review Recently, machine learning has developed rapidly in the field of medicine, playing an important role in disease diagnosis. Our aim of this paper is to provide an…

cs.CV2024

Multi-graph Graph Matching for Coronary Artery Semantic Labeling

Chen Zhao, Zhihui Xu, Pukar Baral +2

Coronary artery disease (CAD) stands as the leading cause of death worldwide, and invasive coronary angiography (ICA) remains the gold standard for assessing vascular anatomical in…

q-bio.GN2023★ 3 cited

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

Chen Zhao, Kuan-Jui Su, Chong Wu +13

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing…

cs.CV2023★ 1 cited

Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling

Chen Zhao, Michele Esposito, Zhihui Xu +1

Coronary artery disease (CAD) is one of the primary causes leading to death worldwide. Accurate extraction of individual arterial branches on invasive coronary angiograms (ICA) is…

cs.CV2023★ 14 cited

Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network

Chen Zhao, Zhihui Xu, Guang-Uei Hung +1

Coronary artery disease (CAD) is one of the primary causes leading deaths worldwide. The presence of atherosclerotic lesions in coronary arteries is the underlying pathophysiologic…

cs.LG2023

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data

Chen Zhao, Anqi Liu, Xiao Zhang +6

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited vi…