I am currently dedicated to developing multimodal foundation models that integrate diverse clinical and imaging data (PET/CT/MRI) for advanced prostate cancer applications.
My expertise spans working with various imaging modalities, including PET, CT, MRI, OCT, and OCTA, to address challenges in disease detection, prognosis, and treatment planning.
I leverage advanced architectures such as Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), and modern foundation models, alongside traditional machine learning methods.
My work involves extensive use of OpenCV for robust image processing and classical computer vision techniques.
I am deeply passionate about the real-world impact and applications of AI, constantly exploring new frontiers.
I am also actively working with Large Language Models (LLMs), Vision-Language Models (VLMs), and Large Multimodal Models (LMMs) to deepen my expertise.
- Languages: Python, C++, MATLAB
- Domains: Medical Imaging, Multimodal AI, Deep Learning, Computer Vision