A5073575600- Machine Learning in Healthcare
- Advanced Neuroimaging Techniques and Applications
- Brain Tumor Detection and Classification
- AI in cancer detection
- Glioma Diagnosis and Treatment
- Medical Imaging and Analysis
- Medical Image Segmentation Techniques
- Mathematical Biology Tumor Growth
- Functional Brain Connectivity Studies
- MRI in cancer diagnosis
- Artificial Intelligence in Healthcare
- Statistical Methods and Inference
- Dementia and Cognitive Impairment Research
Shenzhen University Health Science Center
2018-2021
Shenzhen University
2018-2019
Structural magnetic resonance imaging (sMRI) plays an important role in Alzheimer's disease (AD) detection as it shows morphological changes caused by brain atrophy. Convolutional neural network (CNN) has been successfully used to achieve good performance accurate diagnosis of AD. However, most existing methods utilized shallow CNN structures due the small amount sMRI data, which limits ability learn high-level features. Thus, this paper, we propose a novel unified framework for AD...
Alzheimer's disease (AD) is a neurodegenerative with an irreversible and progressive process. Close monitoring of AD essential for making adjustments in the treatment plan. Since clinical scores can indicate status effectively, prediction based on magnetic resonance imaging (MRI data highly desirable. Different from previous studies at single time point, we propose to build model explore relationship between MRI scores, thereby predicting longitudinal future points corresponding data. The...
Alzheimer's disease (AD), the most common type of dementia, is a progressive neurodegenerative that mainly affects elderly. It causes high financial burden for patients and their families. For effective treatment AD, it important to identify AD progression clinical over time. As cognitive scores can effectively indicate status, prediction using longitudinal magnetic resonance imaging (MRI) data highly desirable. In this paper, we propose joint learning method diagnosis via MRI data....
Tumor growth modeling at macroscopic level from multimodal images can help in predicting the future evolution of tumor and treatment planning. This be achieved using mathematical models where multi time-point are available. In this paper, we propose a coupled modified reaction diffusion model that measures invasion infiltration with biomechanical to consider mass effect. addition, our considers effects radiotherapy and/or chemotherapy if any. The effect is included via log-kill method tissue...