Lecture 3.2.1: DICOM & NIfTI handling, CNN architecturesresNet, densenet , efficient net

In Lecture 3.2.1 of the Masters in Health Data Science program, we dive into the foundations of medical imaging data formats and how they integrate with deep learning (CNN architectures) for clinical AI.

This lecture bridges the gap between raw medical image storage (DICOM & NIfTI) and AI-powered visual intelligence systems used in healthcare.

🔍 What You’ll Learn:
• What is DICOM (Digital Imaging and Communications in Medicine)
• File structure: Header (metadata) + Pixel Data
• Key tags: Patient ID, modality, pixel spacing, orientation
• Understanding Hounsfield Units (HU) and clinical windowing
• What is NIfTI (Neuroimaging Informatics Technology Initiative)
• 3D/4D medical imaging format for research
• Advantages in ML pipelines and neuroimaging
• Medical Imaging vs Standard Images
• High dynamic range (16-bit grayscale vs 8-bit RGB)
• Importance of metadata and spatial context
• Preprocessing Pipeline for Deep Learning
• HU conversion, windowing, normalization, resizing
• Preparing tensors for CNN models
• CNN Architectures for Medical Imaging
• ResNet – Residual learning & skip connections
• DenseNet – Feature reuse & dense connectivity
• EfficientNet – Compound scaling (depth, width, resolution)
• Transfer Learning with ImageNet
• Leveraging pre-trained models for limited medical datasets
• End-to-End Clinical AI Pipeline
• Acquisition → Preprocessing → Augmentation → Inference → Validation

⚕️ Why This Matters:
Medical imaging is not just pixels—it carries clinical, legal, and diagnostic significance. This lecture ensures you understand how to handle imaging data responsibly while building accurate and efficient AI models.

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