189 citations · 719 across the 19 of their papers we have counts for
18 papers
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection. In this paper, we develop a novel BGF-YOLO architecture by inco…
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance o…
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
Ming Kang, Chee-Ming Ting, Fung Fung Ting +1
Blood cell detection is a typical small-scale object detection problem in computer vision. In this paper, we propose a CST-YOLO model for blood cell detection based on YOLOv7 archi…
A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing
Junn Yong Loo, Hwa Hui Tew, Fang Yu Leong +4
Industrial soft sensing is crucial for accurate process monitoring through reliable inference of dominant sensor variables. However, developing effective data-driven soft sensor mo…
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification
Sin-Yee Yap, Junn Yong Loo, Chee-Ming Ting +4
Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging the non-Euclidean top…
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
Yee-Fan Tan, Chee-Ming Ting, Fuad Noman +2
Common measures of brain functional connectivity (FC) including covariance and correlation matrices are semi-positive definite (SPD) matrices residing on a cone-shape Riemannian ma…
BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data
Meini Tang, Chee-Ming Ting, Hernando Ombao
Intrinsic connectivity networks (ICNs) are specific dynamic functional brain networks that are consistently found under various conditions including rest and task. Studies have sho…
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Fuad Noman, Chee-Ming Ting, Hakmook Kang +4
Brain functional connectivity (FC) reveals biomarkers for identification of various neuropsychiatric disorders. Recent application of deep neural networks (DNNs) to connectome-base…
Markov-Switching State-Space Models with Applications to Neuroimaging
David Degras, Chee-Ming Ting, Hernando Ombao
State-space models (SSM) with Markov switching offer a powerful framework for detecting multiple regimes in time series, analyzing mutual dependence and dynamics within regimes, an…
Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI
Chee-Ming Ting, Jeremy I. Skipper, Steven L. Small +1
We consider the challenges in extracting stimulus-related neural dynamics from other intrinsic processes and noise in naturalistic functional magnetic resonance imaging (fMRI). Mos…
Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach
Chee-Ming Ting, S. Balqis Samdin, Meini Tang +1
We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. E…
Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network
Chun-Ren Phang, Chee-Ming Ting, Fuad Noman +1
We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introdu…
Statistical Model for Dynamically-Changing Correlation Matrices with Application to Brain Connectivity
Shih-Gu Huang, S. Balqis Samdin, Chee-Ming Ting +2
Background: Recent studies have indicated that functional connectivity is dynamic even during rest. A common approach to modeling the dynamic functional connectivity in whole-brain…
Short-segment heart sound classification using an ensemble of deep convolutional neural networks
Fuad Noman, Chee-Ming Ting, Sh-Hussain Salleh +1
This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We desig…
Modeling Brain Connectivity with Graphical Models on Frequency Domain
Xu Gao, Weining Shen, Chee-Ming Ting +3
Multichannel electroencephalograms (EEGs) have been widely used to study cortical connectivity during acquisition of motor skills. In this paper, we introduce copula Gaussian graph…
Multi-Scale Factor Analysis of High-Dimensional Brain Signals
Chee-Ming Ting, Hernando Ombao, Sh-Hussain Salleh
In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-…
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Chee-Ming Ting, Hernando Ombao, S. Balqis Samdin +1
Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes…
Exploratory Analysis of High Dimensional Time Series with Applications to Multichannel Electroencephalograms
Yuxiao Wang, Chee-Ming Ting, Hernando Ombao
In this paper, we address the the major hurdle of high dimensionality in EEG analysis by extracting the optimal lower dimensional representations. Using our approach, connectivity…