Deep Learning for Clinical Applications · Image Segmentation · Biosignal Analysis

I specialise in AI-driven medical imaging — building deep learning pipelines for segmentation, classification, and clinical decision support across MRI, CT, X-ray, and EEG modalities.

Medical AI Intern @ Perfint Healthcare — interventional oncology imaging platform
Research Intern @ Sudha Gopalakrishnan Brain Centre, IIT Madras — foetal MRI segmentation, stroke MRI analysis
Research Intern @ Computational Neuroscience & Neural Engineering Lab, IIT Madras — EEG deep learning

🩻 Medical Image Segmentation — 3D U-Net, SegResNet, Attention U-Net (ISLES 2022, MONAI)
🧠 Neuroimaging — NIfTI processing, FSL, ANTs, ITK-SNAP, 3D Slicer, NiftyMIC
📡 Biosignal Analysis — EEG preprocessing, spectral features, MNE, PhysioNet datasets
🔬 Clinical AI Pipelines — end-to-end DL from DICOM/NIfTI to inference, FastAPI deployment

• Stroke lesion segmentation — Separate-Encoder U-Net, mean Dice 0.563 (ISLES 2022)
• Brain tumour detection — MobileNetV2, 90% accuracy, 0.97 AUC
• Pneumonia classifier — CNN deployed via FastAPI

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