Research Focus

Welcome to the AI Medical Imaging Laboratory. Our research focuses on developing innovative artificial intelligence solutions for medical imaging analysis, with particular emphasis on:

Deep Learning

Novel neural network architectures optimized for medical image analysis, including convolutional networks and Large Language Models.

Cardiovascular Imaging

AI-assisted diagnosis of cardiovascular conditions, including Congenitial Heart Disease, coronary artery anomalies of Kawasaki disease, valve regurgitation, and Pulmonary hypertension.

Ophthalmology

Automated detection and classification of retinal diseases and glaucoma from fundus images and numerical data.

Breast Cancer

We work with NTUH on the image segmentation of PET/MR images, labeling tool development, Generative Adversarial Network, and survival predictions.

Selected Publications

Novel Multiple Z-Score Models for Detection of Coronary Artery Dilation: Application in Kawasaki Disease

Kuo, H.C., Chen, S.H., Chen, I.F., Cheng, W.I., Liu, S.F., Guo, M.M.H., Lin, Y.C., and Chen, Y.H. (2024) | Pediatric Rheumatology | IF: 2.8

A Z-score model for the Kawasaki disease to major the size of coronary artery.

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Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images

S. H. Chen, C. W. Wang, I. H. Tai, Ken-Pen Weng, Y. H. Chen, K. S. Hsieh (2021) | International Journal of Interactive Multimedia and Artificial Intelligence | IF: 3.4

A novel deep learning architecture combining YOLOv4 and DenseNet for the automatic detection and classification of coronary artery lesions in echocardiograms for Kawasaki disease diagnosis.

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Blockchain based smart contract for bidding system

S. H. Chen, C. W. Wang, I. H. Tai, Ken-Pen Weng, Y. H. Chen, K. S. Hsieh (2021) | 2018 IEEE International Conference on Applied System Invention (ICASI) | Citations: 130+

A smart contract application in the ETH-like platform.

Artificial Chromosomes with Genetic Algorithm 2 (ACGA2) for Single Machine Scheduling Problems with Sequence-Dependent Setup Times

Chen, S. H.*, M. C. Chen, Y. C. Liou (2014) | Applied Soft Computing Journal | IF: 7.2

An EDA algorithm works with genetic algorithms achieving higher computational Efficiency.

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Addressing the Advantages of Using Ensemble Probabilistic Models in Estimation of Distribution Algorithms for Scheduling Problems

Chen, S. H.*, M. C. Chen (2013) | International Journal of Production Economics | IF: 9.8

Ensemble Probabilistic Models in practice.

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