As a Biomedical Engineer and AI researcher, I specialize in deep learning for medical imaging. I designed Node-U-Net, a lightweight attention-guided network for pulmonary nodule segmentation on the LIDC-IDRI dataset, achieving high accuracy with significantly reduced computational cost. My work demonstrates strong feature discrimination for small nodules and a compelling balance of performance and efficiency.
I am proficient in PyTorch and the full medical AI pipeline—from data preprocessing to model evaluation—and I am passionate about translating research into clinically deployable solutions. I have an ongoing manuscript on Node-U-Net under review at the Journal of Medical Systems (2026), and I’m keen to advance AI-driven diagnostics in healthcare through rigorous experimentation and thoughtful deployment.
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