Module 2: AI Foundations
Core concepts in artificial intelligence for radiology
Machine Learning Fundamentals
Machine learning (ML) is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. In radiology, ML algorithms can be trained to recognize patterns in medical images that may be imperceptible to the human eye.
Key Learning Objectives
- Understand the basic types of machine learning
- Learn about feature extraction and selection
- Recognize common ML algorithms used in medical imaging
Types of Machine Learning
Supervised Learning
Algorithm learns from labeled training data to make predictions or decisions
Examples in Radiology:
- Lesion classification
- Disease detection
- Organ segmentation
Unsupervised Learning
Algorithm identifies patterns and relationships in unlabeled data
Examples in Radiology:
- Anomaly detection
- Patient clustering
- Feature learning
Reinforcement Learning
Algorithm learns optimal actions through trial and error
Examples in Radiology:
- Scan parameter optimization
- Treatment planning
- Adaptive protocols
The Machine Learning Pipeline in Radiology
Figure 1: Typical machine learning workflow for radiological applications
- Data Collection: Gathering diverse, high-quality medical images with appropriate annotations
- Data Preprocessing: Standardizing images, noise reduction, and normalization
- Feature Extraction: Identifying relevant characteristics in images
- Model Training: Using algorithms to learn patterns from the training data
- Model Validation: Testing performance on unseen data
- Deployment: Integrating the model into clinical workflow
Interactive Example: Feature Extraction
In traditional ML approaches, features must be explicitly defined. For a lung nodule detection system, relevant features might include:
Shape Features:
- Diameter
- Volume
- Surface area
- Sphericity
Texture Features:
- Density
- Heterogeneity
- Edge sharpness
- Contrast patterns
Note: Deep learning approaches can automatically learn relevant features from raw image data
Common ML Algorithms in Medical Imaging
| Algorithm | Applications | Advantages |
|---|---|---|
| Support Vector Machines | Classification, regression | Works well with small datasets, handles high-dimensional data |
| Random Forests | Classification, feature importance | Robust to outliers, handles missing values |
| k-Nearest Neighbors | Classification, anomaly detection | Simple, intuitive, no training phase |