
Course 1: AI For Medical Diagnosis
How can AI be applied to medical imaging to diagnose diseases? In this first course, you’ll learn about the nuances of working with both 2D and 3D medical image data, for multi-class classification and image segmentation. You’ll then apply what you’ve learned to classify diseases in x-ray images and segment tumors in 3D MRI brain images. Finally, you’ll learn how to properly evaluate the performance of your models.
Week 1:
- Introduction: A conversation with Andrew Ng
- Diagnosis examples
- Model training on chest x-rays
- Training, prediction, and loss
- Class imbalance
- Binary cross entropy loss function
- Resampling methods
- Multi-task loss
- Transfer learning and data augmentation
- Model testing
Week 2:
- Introduction: A conversation with Andrew Ng
- Evaluation metrics
- Accuracy in terms of conditional probability
- Sensitivity, specificity, and prevalence
- Confusion matrix
- ROC curve
- Threshold (operating point)
- Confidence intervals
- Width of confidence intervals and sample size
- Using a sample to estimate the population
Week 3:
- Introduction: A conversation with Andrew Ng
- Representing MRI data
- Image registration
- 2D and 3D segmentation
- 3D U-Net
- Data augmentation for segmentation
- Loss function for image segmentation
- Soft dice loss
- External validation
- Retrospective vs. prospective data
- Working with cleaned vs. raw data
- Measuring patient outcomes
- Algorithmic bias
- Model influence on medical decision-making