1.       Understand the fundamentals of medical imaging.

2.       Learn the principles of artificial intelligence (AI) and machine learning (ML) in medical applications.

3.       Apply AI techniques to analyze medical images.

4.       Explore current trends and research in AI-driven medical imaging.

Day 1: Introduction to Medical Imaging and AI

• Introduction to Medical Imaging
• Overview of imaging modalities (X-ray, MRI, CT, Ultrasound)
• Basic principles and applications
• Introduction to Artificial Intelligence and Machine Learning
• Overview of AI, ML, and Deep Learning
• AI's role in healthcare and medical imaging
• Ethical Considerations in AI for Healthcare
• Ethical challenges, patient data privacy, and responsible AI usage
• Introduction to Pytorch
• Image Processing Fundamentals
• Basic image processing techniques
• Hands-on: Implementing basic image operations

Day 2: Machine Learning Techniques in Medical Imaging

• Supervised Learning for Image Classification
• Overview of algorithms
• Convolutional Neural Networks (CNNs)
• Introduction to CNNs and applications in image recognition
• Hands-on: Implementing a basic CNN model
• Data Augmentation and Preprocessing
• Techniques to improve model performance
• Hands-on: Applying data augmentation
• Evaluation Metrics for Medical Imaging Models
• Understanding evaluation metrics (accuracy, precision, recall)
• Hands-on Hands-on: Evaluating models with medical image datasets

Day 3: Deep Learning in Medical Imaging

• Advanced CNN Architectures
• Exploration of advanced architectures (ResNet, U-Net)
• Hands-on: Implementing an advanced CNN architecture
• Segmentation in Medical Imaging
• Introduction to image segmentation techniques
• Hands-on: Implementing segmentation using U-Net
• Transfer Learning in Medical Imaging
• Introduction to transfer learning
• Hands-on: Fine-tuning a pre-trained model
• AI in Radiology
• Use cases in radiology
• Discussion on current trends and future directions

Day 4: Practical Applications and Projects

• AI in Diagnostic Imaging
• Case studies and recent research in AI applications
• Project Introduction and Planning
• Overview of the course project
• Group formation and brainstorming
• Project Work
• Hands-on: Data exploration, preprocessing, model training
• Instructor guidance and troubleshooting

Day 5: Advanced Topics

• Explainability and Interpretability in AI Models
• Importance of explainability in medical AI
• Hands-on: Applying interpretability techniques
• Course Recapitulation
• Feedback, and course summary

Quizzes (50%): This component includes multiple assessments administered throughout the course to evaluate your understanding of key concepts. These quizzes are designed to reinforce learning and track progress over time.