output
20202024
most citedGANDA: A deep generative adversarial network predicts the spatial distribution of nanoparticles in tumor pixelly

43 citations

12 papers

physics.chem-ph2024★ 4 cited

Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective

Yuzhi Xu, Haowei Ni, Qinhui Gao +10

Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to c…

cs.CV2023

Adjustable Robust Transformer for High Myopia Screening in Optical Coherence Tomography

Xiao Ma, Zetian Zhang, Zexuan Ji +4

Myopia is a manifestation of visual impairment caused by an excessively elongated eyeball. Image data is critical material for studying high myopia and pathological myopia. Measure…

cs.LG2023

A new method of modeling the multi-stage decision-making process of CRT using machine learning with uncertainty quantification

Kristoffer Larsen, Chen Zhao, Joyce Keyak +7

Aims. The purpose of this study is to create a multi-stage machine learning model to predict cardiac resynchronization therapy (CRT) response for heart failure (HF) patients. This…

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.CV2023★ 2 cited

A new method using deep learning to predict the response to cardiac resynchronization therapy

Kristoffer Larsena, Zhuo He, Chen Zhao +8

Background. Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchroni…

cs.CV2023★ 22 cited

AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms

Chen Zhao, Zhihui Xu, Jingfeng Jiang +4

Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in th…

eess.IV2022★ 10 cited

Automatic reorientation by deep learning to generate short axis SPECT myocardial perfusion images

Fubao Zhu, Guojie Wang, Chen Zhao +6

Single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) can be displayed both in traditional short-axis (SA) cardiac planes and polar maps for interpre…

eess.IV2022★ 33 cited

A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

Fubao Zhu, Jinyu Zhao, Chen Zhao +8

Background: The assessment of left ventricular (LV) function by myocardial perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose of this paper is to develop…

eess.IV2021★ 8 cited

Spatial-temporal V-Net for automatic segmentation and quantification of right ventricles in gated myocardial perfusion SPECT images

Chen Zhao, Shi Shi, Zhuo He +5

Background. Functional assessment of right ventricle (RV) using gated myocardial perfusion single-photon emission computed tomography (MPS) heavily relies on the precise extraction…

physics.med-ph2021★ 15 cited

A method using deep learning to discover new predictors of CRT response from mechanical dyssynchrony on gated SPECT MPI

Zhuo He, Xinwei Zhang, Chen Zhao +5

Background. Studies have shown that the conventional left ventricular mechanical dyssynchrony (LVMD) parameters have their own statistical limitations. The purpose of this study is…

eess.IV2020★ 43 cited

GANDA: A deep generative adversarial network predicts the spatial distribution of nanoparticles in tumor pixelly

Jiulou Zhang, Yuxia Tang, Shouju Wang

Intratumoral nanoparticles (NPs) distribution is critical for the success of nanomedicine in imaging and treatment, but computational models to describe the NPs distribution remain…

cs.HC2020★ 6 cited

OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

Changjian Chen, Jun Yuan, Yafeng Lu +4

One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD…