Intro
Foreword
Preface
Contents
About the Authors
Acronyms
1 Introduction
1.1 Background
1.2 Summary
References
2 Active Learning-What, When, and Where to Deploy?
2.1 Background
2.2 Active Learning Scenarios
2.2.1 Membership Query Synthesis
2.2.2 Stream-Based Selective Sampling
2.2.3 Pool-Based Sampling
2.3 Query Strategies for AL
2.3.1 Uncertainty Sampling
2.3.2 Query by Disagreement
2.3.3 Query by Committee
2.3.4 Estimated Error Reduction
2.3.5 Variance Reduction and Fisher Information Ratio
2.3.6 Density-Weighted Methods

2.4 When and Where to Deploy
2.5 Summary
References
3 Active Learning-Review
3.1 Background
3.2 Querying Techniques
3.3 Existing Custom Querying Techniques
3.4 Used Cases-Medical Imaging Informatics
3.5 Challenges
3.6 Summary
References
4 Active Learning-Methodology
4.1 Background
4.2 Typical Machine Learning (ML) Models
4.2.1 Shallow Learning
4.2.2 Deep Learning
4.3 How Big Data is Big Enough to Begin With?
4.4 Active Learning Versus Passive Learning
4.5 Implementation-The Proposed AL Framework
4.5.1 Introducing Mentoring to ML Models

4.5.2 Unsupervised Learning/Clustering
4.5.3 K-way n-Shot Learning
4.6 Summary
References
5 Active Learning-Validation
5.1 Background
5.2 Datasets
5.2.1 Cough Dataset
5.2.2 Chest CT Scans
5.2.3 Chest X-rays (CXRs)
5.3 Evaluation Metrics
5.3.1 Unsupervised Clustering
5.3.2 Classification
5.4 Validation
5.5 Summary
References
6 Case Study #1: Is My Cough Sound Covid-19?
6.1 Data Preparation
6.2 Results
6.2.1 Clustering
6.2.2 Classification
6.2.3 Comparison
6.3 Summary
References
7 Case Study #2: Reading/Analyzing CT Scans

7.1 Data Preparation
7.2 Results
7.2.1 Clustering
7.2.2 Classification
7.2.3 Comparison
7.3 Summary
References
8 Case Study #3: Reading/Analyzing Chest X-rays
8.1 Data Preparation
8.2 Results
8.2.1 Clustering
8.2.2 Classification
8.2.3 Comparison
8.3 Summary
References
9 Summary and Take-Home Messages
9.1 Background
9.2 Summary
9.3 Take-Home Message
References