A5022686848- Cutaneous Melanoma Detection and Management
- AI in cancer detection
- Multiple Sclerosis Research Studies
- Traffic Prediction and Management Techniques
- Brain Tumor Detection and Classification
- Traffic control and management
- Ultrasound Imaging and Elastography
- Advanced Image and Video Retrieval Techniques
- Gene expression and cancer classification
- Medical Image Segmentation Techniques
- Human Mobility and Location-Based Analysis
- Gaze Tracking and Assistive Technology
- Emotion and Mood Recognition
- Radiomics and Machine Learning in Medical Imaging
- Digital Imaging for Blood Diseases
- Advanced Neuroimaging Techniques and Applications
- Advanced Computing and Algorithms
- Vietnamese History and Culture Studies
- Viral Infections and Immunology Research
- Video Surveillance and Tracking Methods
- Indian and Buddhist Studies
- Visual Attention and Saliency Detection
- Trace Elements in Health
- Functional Brain Connectivity Studies
- Privacy-Preserving Technologies in Data
The University of Sydney
2019-2024
Cooperative Trials Group for Neuro-Oncology
2023-2024
Cognitive Neuroimaging Lab
2022
National University of Defense Technology
2019
Heritage Christian University
2003
As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest the field of artificial intelligence (AI) with increasing availability massive mobility data. Its key challenge lies how to integrate diverse factors (such as temporal rules and spatial dependencies) infer evolution trend flow. To address this problem, we propose unified neural network called Attentive Traffic Flow Machine (ATFM), which can effectively...
Modern management of MS targets No Evidence Disease Activity (NEDA): no clinical relapses, magnetic resonance imaging (MRI) disease activity and disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent and, where appropriate, escalating treatment, standard radiology reports are qualitative may be insensitive development new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate validation. In 397 multi-center scan...
Facial Micro-Expressions (MEs) are spontaneous, involuntary facial movements when a person experiences an emotion but deliberately or unconsciously attempts to conceal his her genuine emotions. Recently, ME recognition has attracted increasing attention due its potential applications such as clinical diagnosis, business negotiation, interrogations, and security. However, it is expensive build large scale datasets, mainly the difficulty of inducing spontaneous MEs. This limits application...
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but scarcity accurately annotated data hinders progress in this area. Obtaining sufficient from a single clinical site is challenging does not address heterogeneous need model robustness. Conversely, collection multiple...
Background and introduction Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative without sharing raw data. Despite great success, FL's applications remain suboptimal in neuroimage tasks such as lesion segmentation multiple sclerosis (MS), due variance characteristics imparted by different scanners acquisition parameters. Methods In this work, we propose the first FL MS framework via two effective re-weighting mechanisms....
Introduction Brain atrophy is a critical biomarker of disease progression and treatment response in neurodegenerative diseases such as multiple sclerosis (MS). Confounding factors inconsistent imaging acquisitions hamper the accurate measurement brain clinic. This study aims to develop validate robust deep learning model overcome these challenges; evaluate its impact on progression. Methods Voxel-wise pseudo-atrophy labels were generated using SIENA, widely adopted tool for MS. Deformation...
Brain atrophy is an important biomarker for monitoring neurodegeneration and disease progression in conditions such as multiple sclerosis (MS). An accurate robust quantitative measurement of brain volume change paramount translational research clinical applications. This paper presents a deep learning based method, DeepBVC, longitudinal using 3D T1-weighted MRI scans. Trained with the intermediate outputs from SIENA, DeepBVC designed to take into account variance caused by different scanners...
Training deep neural networks reliably requires access to large-scale datasets. However, obtaining such datasets can be challenging, especially in the context of neuroimaging analysis tasks, where cost associated with image acquisition and annotation prohibitive. To mitigate both time financial costs model development, a clear understanding amount data required train satisfactory is crucial. This paper focuses on an early stage phase learning research, prior proposes strategic framework for...
Multiple sclerosis (MS) is a neurodegenerative disease of the central nerve system (CNS), which has potential to cause neurological disability, particularly for young adults. Recently, deep learning-based techniques are important MS diagnosis and treatment, since they can segment lesions caused by automatically accurately. However, their applicability multi-center scenarios limited, due privacy security issues in data sharing. To tackle these limitations, decentralized learning framework...
Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative without sharing raw data. Despite great success, FL's performance is limited multiple sclerosis (MS) lesion segmentation tasks, due variance in characteristics imparted by different scanners and acquisition parameters. In this work, we propose the first FL MS framework via two effective re-weighting mechanisms. Specifically, a learnable weight assigned each local node during...
Existing anchor-based and anchor-free object detectors in multi-stage or one-stage pipelines have achieved very promising detection performance. However, they still encounter the design difficulty hand-crafted 2D anchor definition learning complexity 1D direct location regression. To tackle these issues, this paper, we propose a novel detector coined as ScopeNet, which models anchors of each mutually dependent relationship. This approach quantizes prediction space employs coarse-to-fine...
Brain atrophy is an important biomarker for monitoring neurodegeneration and disease progression in conditions such as multiple sclerosis (MS). An accurate robust quantitative measurement of brain volume change paramount translational research clinical applications. This paper presents a deep learning based method, DeepBVC, longitudinal using 3D T1-weighted MRI scans. Trained with the intermediate outputs from SIENA, DeepBVC designed to take into account variance caused by different scanners...
ABSTRACT Modern management of MS targets No Evidence Disease Activity (NEDA): no clinical relapses, magnetic resonance imaging (MRI) disease activity and disability worsening. While MRI is the principal tool available to neurologists for monitoring clinically silent and, where appropriate, escalating treatment, standard radiology reports are qualitative may be insensitive development new or enlarging lesions. Existing quantitative neuroimaging tools lack adequate validation. In 397...
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but scarcity accurately annotated data hinders progress in this area. Obtaining sufficient from a single clinical site is challenging does not address heterogeneous need model robustness. Conversely, collection multiple...
Facial Micro-Expressions (MEs) are spontaneous, involuntary facial movements when a person experiences an emotion but deliberately or unconsciously attempts to conceal his her genuine emotions. Recently, ME recognition has attracted increasing attention due its potential applications such as clinical diagnosis, business negotiation, interrogations, and security. However, it is expensive build large scale datasets, mainly the difficulty of inducing spontaneous MEs. This limits application...
As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest the field of artificial intelligence (AI) with increasing availability massive mobility data. Its key challenge lies how to integrate diverse factors (such as temporal rules and spatial dependencies) infer evolution trend flow. To address this problem, we propose unified neural network called Attentive Traffic Flow Machine (ATFM), which can effectively...