AI in Medical Imaging – AIHC Courses

Course Description

This course provides an in-depth exploration of artificial intelligence applications in medical imaging and radiology. It covers advanced techniques in image analysis, interpretation, computer-aided detection and diagnosis, image reconstruction and enhancement, and workflow optimization. The course is designed for radiologists, medical imaging professionals, AI researchers, and healthcare technologists

Course Content

Introduction to AI in Medical Imaging 4 Topics

Expand

Lesson Content

0% Complete 0/4 Steps

Overview of AI and machine learning in healthcare

Historical perspective of AI in radiology

Current state and future trends of AI in medical imaging Ethical considerations and challenges

Ethical considerations and challenges

Fundamentals of Medical Image Processing 4 Topics

Expand

Lesson Content

0% Complete 0/4 Steps

Digital image representation and formats in radiology (DICOM, NIfTI)

Basic image processing techniques (filtering, segmentation, registration)

Feature extraction and representation

Introduction to deep learning for image analysis

AI Applications in Image Analysis and Interpretation 4 Topics

Expand

Lesson Content

0% Complete 0/4 Steps

Convolutional Neural Networks (CNNs) for medical image analysis

Transfer learning and fine-tuning pre-trained models for radiology

Case studies: AI in chest X-ray interpretation, brain MRI analysis, and mammography

Multimodal learning for integrating different imaging modalities

Computer-Aided Detection and Diagnosis (CAD) 5 Topics

Expand

Lesson Content

0% Complete 0/5 Steps

Principles of CAD systems in radiology

Machine learning algorithms for lesion detection and classification

Deep learning approaches for automated diagnosis

Performance evaluation metrics for CAD systems (sensitivity, specificity, FROC analysis)

Integration of CAD systems into clinical workflows

AI for Image Reconstruction and Enhancement 5 Topics

Expand

Lesson Content

0% Complete 0/5 Steps

Fundamentals of image reconstruction in CT, MRI, and PET

Deep learning-based image reconstruction techniques

Super-resolution and denoising in medical imaging

AI-powered contrast enhancement and artifact reduction

Quantitative imaging and radiomics

Workflow Optimization in Radiology Departments 5 Topics

Expand

Lesson Content

0% Complete 0/5 Steps

AI-driven worklist prioritization and triage

Automated report generation and structured reporting

Natural Language Processing (NLP) for radiology reports

AI-assisted quality control and peer review

Integration of AI tools with PACS and RIS systems

Advanced AI Techniques in Radiology 4 Topics

Expand

Lesson Content

0% Complete 0/4 Steps

Generative Adversarial Networks (GANs) for synthetic image generation

Federated learning for multi-institutional collaboration

Explainable AI and visualization techniques for radiologists

AI in interventional radiology and image-guided procedures

Clinical Validation and Deployment of AI Models 4 Topics

Expand

Lesson Content

0% Complete 0/4 Steps

Design and implementation of clinical validation studies

Regulatory pathways for AI-based medical devices (FDA, CE marking)

Best practices for AI model deployment in clinical settings

Continuous monitoring and updating of AI models

AI-Powered Precision Medicine in Radiology 4 Topics

Expand

Lesson Content

0% Complete 0/4 Steps

Radiogenomics and imaging biomarkers

AI for treatment response prediction and monitoring

Personalized risk assessment using imaging and non-imaging data

AI in radiotherapy planning and dose optimization

Future Directions and Emerging Technologies 4 Topics

Expand

Lesson Content

0% Complete 0/4 Steps

Quantum computing applications in medical imaging

AI in molecular imaging and theranostics

Integration of AI with other emerging technologies (AR/VR, 3D printing)

The role of AI in population health management and screening programs

About Instructor

AIHC Office

3 Courses