Module #1 Introduction to AI in Medical Imaging and Radiology Overview of the role of AI in medical imaging and radiology, including its history, current applications, and future directions.
Module #2 Fundamentals of Machine Learning Basic concepts of machine learning, including supervised and unsupervised learning, neural networks, and deep learning.
Module #3 Medical Image Acquisition and Processing Overview of medical image acquisition modalities (e.g. X-ray, CT, MRI, US) and image processing techniques (e.g. filtering, segmentation).
Module #4 Image Analysis Techniques Introduction to image analysis techniques, including feature extraction, object detection, and image registration.
Module #5 Deep Learning for Medical Image Analysis Application of deep learning techniques to medical image analysis, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Module #6 Image Segmentation with Deep Learning Deep learning-based approaches for image segmentation, including U-Net and its variants.
Module #7 Image Classification with Deep Learning Deep learning-based approaches for image classification, including classification of diseases and anomalies.
Module #8 Image Registration and Fusion Image registration and fusion techniques, including mutual information and mutual entropy.
Module #9 AI for Disease Detection and Diagnosis AI-based approaches for disease detection and diagnosis, including applications in cancer, cardiovascular, and neurological diseases.
Module #10 AI for Image-Guided Interventions AI-based approaches for image-guided interventions, including biopsies and tumor ablation.
Module #11 AI for Radiology Workflow Optimization AI-based approaches for radiology workflow optimization, including automating image analysis and prioritizing cases.
Module #12 AI for Radiation Therapy Planning AI-based approaches for radiation therapy planning, including automated treatment planning and quality assurance.
Module #13 Ethical and Regulatory Considerations Ethical and regulatory considerations for AI in medical imaging and radiology, including data privacy and algorithm bias.
Module #14 Clinical Validation and Evaluation Clinical validation and evaluation of AI models for medical imaging and radiology, including metrics and benchmarks.
Module #15 Case Studies in AI-Assisted Radiology Real-world examples of AI-assisted radiology, including applications in cancer diagnosis, cardiovascular disease, and neurological disorders.
Module #16 AI-Assisted Radiology in Clinical Practice Implementation and integration of AI-assisted radiology in clinical practice, including workflow and training considerations.
Module #17 Future Directions and Emerging Trends Future directions and emerging trends in AI for medical imaging and radiology, including multi-modal imaging and Explainable AI.
Module #18 Computational Anatomy and Biomechanics Introduction to computational anatomy and biomechanics, including shape analysis and simulation.
Module #19 AI for Medical Image Compression and Reconstruction AI-based approaches for medical image compression and reconstruction, including applications in CT and MRI.
Module #20 AI-Assisted Radiology for Rare Diseases AI-assisted radiology for rare diseases, including applications in Orphanet and rare cancer diagnosis.
Module #21 AI for Radiology Education and Training AI-based approaches for radiology education and training, including simulation-based training and AI-assisted feedback.
Module #22 Radiology and AI:A Systematic Review Systematic review of AI applications in radiology, including a critical evaluation of existing literature.
Module #23 AI-Assisted Radiology for Global Health AI-assisted radiology for global health, including applications in low-resource settings and underserved populations.
Module #24 AI for Medical Imaging and Radiology Research AI-based approaches for medical imaging and radiology research, including data-driven discovery and hypothesis generation.
Module #25 Course Wrap-Up & Conclusion Planning next steps in AI in Medical Imaging and Radiology career
