Search Results for author: Chee-Ming Ting

Found 21 papers, 9 papers with code

SFC-GAN: A Generative Adversarial Network for Brain Functional and Structural Connectome Translation

no code implementations13 Jan 2025 Yee-Fan Tan, Jun Lin Liow, Pei-Sze Tan, Fuad Noman, Raphael C. -W. Phan, Hernando Ombao, Chee-Ming Ting

Modern brain imaging technologies have enabled the detailed reconstruction of human brain connectomes, capturing structural connectivity (SC) from diffusion MRI and functional connectivity (FC) from functional MRI.

Functional Connectivity Generative Adversarial Network +1

ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network for Soft Sensing

1 code implementation2 Jan 2025 Hwa Hui Tew, Fan Ding, Gaoxuan Li, Junn Yong Loo, Chee-Ming Ting, Ze Yang Ding, Chee Pin Tan

Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges.

KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes

no code implementations2 Jan 2025 Hwa Hui Tew, Gaoxuan Li, Fan Ding, Xuewen Luo, Junn Yong Loo, Chee-Ming Ting, Ze Yang Ding, Chee Pin Tan

Furthermore, the analysis shows that KANS can find sensors closely related to different process variables without domain knowledge, significantly improving soft sensing accuracy.

Graph Attention Graph structure learning +1

PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices

1 code implementation29 Oct 2024 Ming Kang, Fung Fung Ting, Raphaël C. -W. Phan, Chee-Ming Ting

In this paper, we propose a new You Only Look Once (YOLO)-based detection model that incorporates Pretrained Knowledge (PK), called PK-YOLO, to improve the performance for brain tumor detection in multiplane MRI slices.

Object object-detection +1

Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling

no code implementations21 Jul 2024 Junn Yong Loo, Michelle Adeline, Arghya Pal, Vishnu Monn Baskaran, Chee-Ming Ting, Raphael C. -W. Phan

Energy based models (EBMs) are appealing for their generality and simplicity in data likelihood modeling, but have conventionally been difficult to train due to the unstable and time-consuming implicit MCMC sampling during contrastive divergence training.

Image Generation Unconditional Image Generation

A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

1 code implementation22 Apr 2024 Ming Kang, Fung Fung Ting, Raphaël C. -W. Phan, ZongYuan Ge, Chee-Ming Ting

Our ablation study demonstrates the importance of the proposed modules with CNN-Transformer networks and the convolutional blocks in Transformer for improving the performance of brain tumor segmentation with missing modalities.

Brain Tumor Segmentation Segmentation +1

Dynamic MRI reconstruction using low-rank plus sparse decomposition with smoothness regularization

no code implementations30 Jan 2024 Chee-Ming Ting, Fuad Noman, Raphaël C. -W. Phan, Hernando Ombao

The low-rank plus sparse (L+S) decomposition model has enabled better reconstruction of dynamic magnetic resonance imaging (dMRI) with separation into background (L) and dynamic (S) component.

MRI Reconstruction

ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation

1 code implementation11 Dec 2023 Ming Kang, Chee-Ming Ting, Fung Fung Ting, Raphaël C. -W. Phan

We propose a novel Attentional Scale Sequence Fusion based You Only Look Once (YOLO) framework (ASF-YOLO) which combines spatial and scale features for accurate and fast cell instance segmentation.

Instance Segmentation Position +2

RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection

1 code implementation31 Jul 2023 Ming Kang, Chee-Ming Ting, Fung Fung Ting, Raphaël C. -W. Phan

With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection.

Medical Diagnosis medical image detection +3

Unsupervised Cross-Domain Soft Sensor Modelling via Deep Physics-Inspired Particle Flow Bayes

no code implementations8 Jun 2023 Junn Yong Loo, Ze Yang Ding, Surya G. Nurzaman, Chee-Ming Ting, Vishnu Monn Baskaran, Chee Pin Tan

To address these challenges, we propose a deep Particle Flow Bayes (DPFB) framework for cross-domain soft sensor modeling in the absence of target state labels.

Missing Labels Sensor Modeling +2

A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

1 code implementation14 Feb 2023 Sin-Yee Yap, Junn Yong Loo, Chee-Ming Ting, Fuad Noman, Raphael C. -W. Phan, Adeel Razi, David L. Dowe

In this paper, a deep spatiotemporal variational Bayes (DSVB) framework is proposed to learn time-varying topological structures in dynamic FC networks for identifying autism spectrum disorder (ASD) in human participants.

Functional Connectivity Graph Embedding +1

Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation

no code implementations10 Dec 2022 Yee-Fan Tan, Chee-Ming Ting, Fuad Noman, Raphaël C. -W. Phan, Hernando Ombao

Despite its remarkable success for Euclidean-valued data generation, use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC.

Data Augmentation Functional Connectivity

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification

no code implementations27 Jul 2021 Fuad Noman, Chee-Ming Ting, Hakmook Kang, Raphael C. -W. Phan, Brian D. Boyd, Warren D. Taylor, Hernando Ombao

Our new framework demonstrates feasibility of learning graph embeddings on brain networks to provide discriminative information for diagnosis of brain disorders.

Functional Connectivity Graph Embedding

Separating Stimulus-Induced and Background Components of Dynamic Functional Connectivity in Naturalistic fMRI

1 code implementation24 Jan 2021 Chee-Ming Ting, Jeremy I. Skipper, Steven L. Small, Hernando Ombao

We propose a novel, data-driven approach based on low-rank plus sparse (L+S) decomposition to isolate stimulus-driven dynamic changes in brain functional connectivity (FC) from the background noise, by exploiting shared network structure among subjects receiving the same naturalistic stimuli.

Functional Connectivity

Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach

no code implementations9 Apr 2020 Chee-Ming Ting, S. Balqis Samdin, Meini Tang, Hernando Ombao

We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time.

Community Detection Stochastic Block Model

Classification of EEG-Based Brain Connectivity Networks in Schizophrenia Using a Multi-Domain Connectome Convolutional Neural Network

no code implementations21 Mar 2019 Chun-Ren Phang, Chee-Ming Ting, Fuad Noman, Hernando Ombao

We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ).

EEG Functional Connectivity +1

Short-segment heart sound classification using an ensemble of deep convolutional neural networks

no code implementations27 Oct 2018 Fuad Noman, Chee-Ming Ting, Sh-Hussain Salleh, Hernando Ombao

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats.

General Classification Sound Classification +1

A Markov-Switching Model Approach to Heart Sound Segmentation and Classification

no code implementations10 Sep 2018 Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, S. Balqis Samdin, Hernando Ombao, Hadri Hussain

Methods: We propose an approach based on Markov switching autoregressive model (MSAR) to segmenting the HS into four fundamental components each with distinct second-order structure.

General Classification

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