Medical Image Analysis

Medical image analysis is the intersection of deep learning and radiology. You take medical images (CT scans, MRI, X-rays, ultrasound), preprocess them, train neural networks to detect or segment abnormalities (tumors, fractures, pneumonia), and deploy models as clinical decision support tools. The workflow: DICOM → preprocessing (resampling, normalization, augmentation) → model training (U-Net for segmentation, ResNet/EfficientNet for classification) → validation (clinical evaluation, statistical comparison to radiologist baseline) → deployment (integration into PACS, regulatory clearance). It bridges machine learning, medical domain knowledge, and regulatory compliance.