Module #1 Introduction to Medical Image Analysis Overview of medical imaging modalities, challenges, and applications of machine learning
Module #2 Machine Learning Fundamentals Introduction to machine learning concepts, supervised and unsupervised learning, and evaluation metrics
Module #3 Mathematical Prerequisites Review of linear algebra, calculus, and probability theory relevant to machine learning
Module #4 Image Processing Techniques Filtering, thresholding, segmentation, and registration methods for medical images
Module #5 Feature Extraction and Selection Handcrafted and deep learning-based feature extraction methods for medical images
Module #6 Image Preprocessing and Normalization Importance and techniques for preprocessing and normalizing medical images
Module #7 Introduction to Supervised Learning Concepts of supervised learning, classification, and regression
Module #8 Convolutional Neural Networks (CNNs) for Image Classification Designing and training CNNs for medical image classification tasks
Module #9 Regression and Segmentation with Supervised Learning Applying supervised learning to medical image regression and segmentation tasks
Module #10 Deep Learning Fundamentals Introduction to deep learning concepts, activation functions, and optimization techniques
Module #11 Deep Learning Architectures for Medical Imaging Designing and training deep learning models for medical image analysis
Module #12 Transfer Learning and Fine-tuning for Medical Imaging Applying transfer learning and fine-tuning to medical image analysis tasks
Module #13 Introduction to Unsupervised Learning Concepts of unsupervised learning, clustering, and dimensionality reduction
Module #14 Unsupervised Learning for Medical Image Analysis Applying unsupervised learning to medical image segmentation, feature extraction, and anomaly detection
Module #15 Semi-supervised Learning for Medical Imaging Using semi-supervised learning to leverage limited labeled data in medical image analysis
Module #16 Multimodal Medical Image Analysis Analyzing and fusing multiple medical imaging modalities
Module #17 Explainable AI for Medical Imaging Interpretable machine learning for medical image analysis
Module #18 Real-world Applications and Case Studies Case studies and applications of machine learning in medical imaging, including challenges and limitations
Module #19 Implementing Machine Learning Models for Medical Imaging Practical considerations for implementing machine learning models using popular libraries and frameworks
Module #20 Evaluating and Validating Machine Learning Models Metrics and techniques for evaluating and validating machine learning models for medical image analysis
Module #21 Medical Image Analysis Pipelines and Workflows Designing and implementing pipelines and workflows for medical image analysis tasks
Module #22 Cardiac Image Analysis Machine learning for cardiac image analysis, including segmentation, classification, and regression
Module #23 Neuroimaging Analysis Machine learning for neuroimaging analysis, including segmentation, classification, and regression
Module #24 Radiogenomics and Imaging Biomarkers Machine learning for radiogenomics and imaging biomarkers in cancer diagnosis and treatment
Module #25 Ethics in Medical Image Analysis Ethical considerations for machine learning in medical image analysis
Module #26 Future Directions and Emerging Trends Emerging trends and future directions in machine learning for medical image analysis
Module #27 Conclusion and Next Steps Conclusion and next steps for applying machine learning to medical image analysis
Module #28 Project Development Guidelines Guidelines for developing a machine learning project for medical image analysis
Module #29 Case Study 1:[Insert Case Study Topic] In-depth case study of a machine learning project for medical image analysis
Module #30 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Medical Image Analysis career
