A5088178652- Medical Image Segmentation Techniques
- AI in cancer detection
- Domain Adaptation and Few-Shot Learning
- Fetal and Pediatric Neurological Disorders
- Generative Adversarial Networks and Image Synthesis
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neural Network Applications
- Brain Tumor Detection and Classification
- Cleft Lip and Palate Research
- Advanced MRI Techniques and Applications
- Face recognition and analysis
- Advanced Image Processing Techniques
- COVID-19 diagnosis using AI
- Artificial Intelligence in Healthcare and Education
- Medical Imaging and Analysis
- Robotics and Sensor-Based Localization
- Neonatal and fetal brain pathology
- 3D Shape Modeling and Analysis
- Machine Learning and Data Classification
- Machine Learning in Healthcare
- Ultrasound Imaging and Elastography
- Coal Properties and Utilization
- Advanced Neuroimaging Techniques and Applications
- Hydrocarbon exploration and reservoir analysis
- Image Retrieval and Classification Techniques
University of Leeds
2021-2025
University of Manchester
2024-2025
Simulation Technologies (United States)
2024
Jilin University
2023
Shenzhen University Health Science Center
2018-2022
Shandong University of Science and Technology
2022
Shenzhen University
2019-2021
Boston Children's Hospital
2019-2020
Harvard University
2019-2020
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary inhomogeneous intensity distribution TRUS, as well large variability shapes. This paper develops a novel 3D deep neural network equipped with attention modules better TRUS by fully exploiting complementary information encoded...
Early diagnosis of cleft lip and palate (CLP) requires a multiplane examination, demanding high technical proficiency from radiologists. Therefore, this study aims to develop validate the first artificial intelligence (AI)-based model (CLP-Net) for fully automated multi-plane localization in three-dimensional(3D) ultrasound during trimester. This retrospective included 418 (394 normal, 24 CLP) 3D 288 pregnant woman between July 2022 October 2024 Shenzhen Guangming District People's Hospital...
Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with single ultrasound modality has always been a challenge. To achieve this, we proposed two-stage grading system to automatically evaluate breast tumors from images five based on convolutional neural networks (CNNs).This new developed automatic was consisted of two stages, including tumor identification grading. The constructed network for identification, denoted as ROI-CNN, can identify...
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and folding. Manual the plate, or manual refinement automatic segmentations tedious time-consuming. Automatic on other hand, challenged by relatively low resolution reconstructed MRI scans compared to thin structure partial voluming, wide range variations morphology as matures during gestation. To reduce burden segmentations, we have developed a new powerful deep learning method. Our method...
Deep learning models represent the state of art in medical image segmentation. Most these are fully-convolutional networks (FCNs), namely each layer processes output preceding with convolution operations. The operation enjoys several important properties such as sparse interactions, parameter sharing, and translation equivariance. Because properties, FCNs possess a strong useful inductive bias for modeling analysis. However, they also have certain shortcomings, performing fixed...
Deep Neural Networks (DNNs) suffer from the performance degradation when image appearance shift occurs, especially in ultrasound (US) segmentation. In this paper, we propose a novel and intuitive framework to remove shift, hence improve generalization ability of DNNs. Our work has three highlights. First, follow spirit universal style transfer shifts, which was not explored before for US images. Without sacrificing structure details, it enables arbitrary style-content transfer. Second,...
Accurate standard plane (SP) localization is the fundamental step for prenatal ultrasound (US) diagnosis. Typically, dozens of US SPs are collected to determine clinical 2D has perform scanning each SP, which time-consuming and operator-dependent. While 3D containing multiple in one shot inherent advantages less user-dependency more efficiency. Automatically locating SP very challenging due huge search space large fetal posture variations. Our previous study proposed a deep reinforcement...
Volumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Segmentation landmark localization two key techniques making quantitative evaluation of volumes available clinic. However, both tasks non-trivial when considering poor image quality, boundary ambiguity anatomical variations volumetric ultrasound. In this paper, we propose an effective framework for simultaneous...