🟢 Beginner Stage: Foundations of AI in Medical Imaging

🎯 Objective

To build foundational knowledge in AI-driven medical imaging, focusing on classic architectures, image understanding tasks, and essential ethical and technical considerations.

🧠 Topics

Topic	Description
Segmentation of Medical Images	Learn pixel-wise classification with U-Net and nnU-Net, used for delineating anatomical structures.
Classification of Medical Images	Apply CNNs such as ResNet and EfficientNet for diagnosing conditions from X-rays, CTs, and MRIs.
Registration of Medical Images	Understand techniques to align images taken across modalities or time for improved analysis.
Feature Extraction from Medical Images	Extract visual features used for clustering, annotation, or simple ML pipelines.
Transfer Learning	Reuse pre-trained models to accelerate training and improve performance on small datasets.
Explainability & Interpretability	Explore tools like SHAP, Grad-CAM, and LIME to interpret AI decisions.
AI Bias & Fairness	Learn how to detect, evaluate, and mitigate bias in medical datasets and models.
Multi-Modal Learning (Intro)	An overview of combining modalities like images and EHR data.
Federated Learning (Intro)	Introductory concepts of decentralized model training while preserving data privacy.