no code implementations • 13 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.
1 code implementation • 2 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.
no code implementations • 2 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.
1 code implementation • 29 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.
no code implementations • 21 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.
1 code implementation • 22 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.
no code implementations • 30 Jan 2024 • Ming Kang, Chee-Ming Ting, Fung Fung Ting, Raphaël Phan
Medical image semantic segmentation techniques can help identify tumors automatically from computed tomography (CT) scans.
no code implementations • 30 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.
1 code implementation • 11 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.
1 code implementation • 22 Sep 2023 • Ming Kang, Chee-Ming Ting, Fung Fung Ting, Raphaël C. -W. Phan
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection.
1 code implementation • 31 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.
1 code implementation • 26 Jun 2023 • Ming Kang, Chee-Ming Ting, Fung Fung Ting, Raphaël Phan
Blood cell detection is a typical small-scale object detection problem in computer vision.
no code implementations • 8 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.
1 code implementation • 14 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.
no code implementations • 10 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.
no code implementations • 27 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.
1 code implementation • 24 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.
no code implementations • 9 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.
no code implementations • 21 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).
no code implementations • 27 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.
no code implementations • 10 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.