About My Research
I am currently a Master’s student at Chang Gung University (CGU), specializing in advanced neuroimaging analysis and clinical prediction models. My work resides at the intersection of Medical Imaging and Artificial Intelligence, with a mission to develop more sensitive and accurate diagnostic tools for neurological conditions.
Core Research Focus
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Advanced Diffusion MRI Analysis: Investigating microstructural brain changes using sophisticated dMRI techniques, including Diffusion Tensor Imaging (DTI) and Fixel-based analysis (FBA). I have extensive experience interpreting metrics such as FDC (Fiber Density and Cross-section) to understand brain connectivity.
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Machine Learning in Clinical Prediction: Developing hierarchical machine learning models (e.g., Random Forest, XGBoost) to predict functional recovery in patients six months after neurological events. I focus on model interpretability and consistency to bridge the gap between AI and clinical decision-making.
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Generative Models for Medical Imaging: Validating the potential of Generative Adversarial Networks (GANs), specifically Pix2Pix, to synthesize high-quality brain images and enhance the detection of microstructural abnormalities.
Technical Toolkit
- Neuroimaging: MRtrix3, FSL, TractSeg.
- Machine Learning: Weka, Scikit-learn, Python.
- Organization & Productivity: Professional academic literature management via Notion.
Current Endeavors
I am currently an intern in Okazaki, Japan, where I am further expanding my research horizons and collaborating with international experts in the field of brain signal communication and structure-function relationships.