Dedicated researcher with expertise in applying advanced image processing and deep learning techniques to medical imaging, particularly in MRI geometric distortion correction, PET partial volume correction, and quantitative image analysis. My research spans MRI, PET, and their applications in neurodegenerative disease diagnosis. I aim to further advance the fields of medical image processing, reconstruction, and personalized medicine through innovative AI-driven approaches, with a particular focus on improving diagnostic accuracy for neurodegenerative diseases using multimodal imaging techniques.
Skills:
▪ Programming: Python (advanced), MATLAB (advanced)
▪ Deep Learning Frameworks: TensorFlow, PyTorch, Pyradiomics
▪ Medical Imaging Software: FSL, PMOD, SPM, 3DSlicer
▪ Simulation Tools: COMSOL Multiphysics, MCNP
▪ Statistical Analysis: SPSS, R, MedCalc
▪ Machine Learning and Deep Learning algorithm development
▪ Advanced image processing and analysis techniques
▪ Quantitative image analysis and radiomics feature extraction
▪ Data collection, cleaning, and analysis
▪ Scientific writing and presentation
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Academic Profile
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AutoCorNN: an unsupervised physics-aware deep learning model for geometric distortion correction of brain MRI images towards MR-only stereotactic radiosurgery.First author
Comparative assessment of attention-based deep learning and non-local mean filtering for joint noise reduction and partial volume correction in low-dose PET imagingFourth author
Assessing the Stability and Discriminative Ability of Radiomics Features in the Tumor Microenvironment: Leveraging Peri-Tumoral Regions in Vestibular Schwannoma.First author