Module #1 Introduction to Medical Imaging Analysis Overview of medical imaging analysis, importance of deep learning, and course objectives
Module #2 Basics of Deep Learning Review of deep learning fundamentals, including neural networks, supervised and unsupervised learning, and popular deep learning frameworks
Module #3 Medical Imaging Modalities Overview of common medical imaging modalities, including X-ray, CT, MRI, and Ultrasound
Module #4 Image Preprocessing Techniques Introduction to image preprocessing techniques, including normalization, filtering, and resampling
Module #5 Convolutional Neural Networks (CNNs) Fundamentals In-depth look at CNNs, including architecture, convolutional and pooling layers, and activation functions
Module #6 CNNs for Medical Image Classification Applying CNNs to medical image classification tasks, including binary and multi-class classification
Module #7 Semantic Segmentation with CNNs Using CNNs for semantic segmentation tasks, including pixel-wise classification and object detection
Module #8 Object Detection in Medical Images Techniques for object detection in medical images, including YOLO, SSD, and Faster R-CNN
Module #9 Image Registration and Alignment Introduction to image registration and alignment techniques, including feature-based and intensity-based methods
Module #10 Image Segmentation with Active Contours Using active contours for image segmentation, including snakes and level sets
Module #11 Deep Learning for Image Denoising Applying deep learning techniques to image denoising tasks, including autoencoders and generative adversarial networks (GANs)
Module #12 Deep Learning for Image Reconstruction Using deep learning for image reconstruction tasks, including compressed sensing and super-resolution
Module #13 Transfer Learning for Medical Imaging Using pre-trained models and fine-tuning for medical imaging analysis tasks
Module #14 Ensemble Learning for Medical Imaging Combining multiple models for improved performance in medical imaging analysis tasks
Module #15 Uncertainty Quantification in Deep Learning Techniques for quantifying uncertainty in deep learning models, including Bayesian neural networks and Monte Carlo dropout
Module #16 Explainability and Interpretability in Deep Learning Techniques for explaining and interpreting deep learning models, including saliency maps and feature importance
Module #17 Ethical Considerations in Medical Imaging Analysis Ethical considerations and potential pitfalls in medical imaging analysis, including bias and fairness
Module #18 Medical Imaging Analysis Pipeline Design Designing a comprehensive pipeline for medical imaging analysis, including data ingestion, preprocessing, and model deployment
Module #19 Deep Learning for Retinal Imaging Analysis Applying deep learning techniques to retinal imaging analysis tasks, including disease diagnosis and image segmentation
Module #20 Deep Learning for Mammography Analysis Using deep learning for mammography analysis tasks, including breast cancer detection and image segmentation
Module #21 Deep Learning for MRI Analysis Applying deep learning techniques to MRI analysis tasks, including image segmentation, registration, and disease diagnosis
Module #22 Deep Learning for Ultrasound Imaging Analysis Using deep learning for ultrasound imaging analysis tasks, including fetal development and disease diagnosis
Module #23 Deep Learning for CT Scan Analysis Applying deep learning techniques to CT scan analysis tasks, including image segmentation, registration, and disease diagnosis
Module #24 Deep Learning for X-ray Imaging Analysis Using deep learning for X-ray imaging analysis tasks, including disease diagnosis and image segmentation
Module #25 Course Wrap-Up & Conclusion Planning next steps in Deep Learning for Medical Imaging Analysis career
