Syllabus

Introduction and Fundamental Concepts

Introduction to Medical Imaging Systems
Principles of systems such as MRI, CT, PET, X-Ray, and Ultrasound.
Key aspects of image reconstruction for each modality.
Overview of challenges and characteristics of medical images.
Fundamentals of Medical Image Processing
Introduction to different types of imaging data.
Specific challenges in medical image analysis compared to standard image processing.

Medical Image Preprocessing

• Preprocessing Methods for Medical Images
• Noise removal, contrast enhancement, and sharpening.
• Normalization and standardization techniques.

Deep Learning and Related Architectures

• Overview of Deep Learning and Neural Networks
• Introduction to Convolutional Neural Networks (CNNs).
• Generative models (GANs) and their applications in medical image synthesis.
• Transformer models and their role in medical image processing.
• Applications of Deep Learning in Medical Image Processing
• Segmentation of medical images
• Image quality enhancement techniques
• Registration and alignment of medical images

Advanced Applications:

• Reconstruction of Medical Images Using Deep Learning
• Methods for reconstructing incomplete or noisy images.
• Applications of generative models in medical image reconstruction.
• Generative Models and Semi-Supervised Learning
• Leveraging limited or partially labeled data.
• Techniques such as Consistency Regularization and Pseudo-Labeling.
• Explainability in Deep Learning Architectures
• Explainability in CNN, GAN, and Transformer architectures.
• Challenges and solutions for providing interpretability in deep learning models.

 Modern Concepts and Future Trends

• Foundation Models
• Introduction to foundation models and their applications in medical image analysis.
• Exploration of multimodal models.
• Using Large Language Models (LLMs)
• Integrating text and image data for medical data analysis.
• Applications of LLMs in medical image classification and interpretation.