Deep Learning for Digital Pathology in Limited Data Scenarios.
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Linköping :
Linkopings Universitet,
2022.
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| Edition: | 1st ed. |
| Series: | Linköping Studies in Science and Technology. Licentiate Thesis Series
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| Subjects: | |
| Online Access: | Click to View |
Table of Contents:
- Intro
- Abstract
- Populärvetenskaplig sammanfattning
- Acknowledgments
- List of Publications
- Contributions
- Contents
- I Comprehensive Summary
- 1 Introduction
- 1.1 Digital pathology
- 1.2 Deep learning
- 1.3 Objectives and contributions
- 1.4 Thesis outline
- 2 Background
- 2.1 Medical images
- 2.2 Deep learning
- 2.3 Application on medical image data
- 3 Building robust models
- 3.1 Domain shift
- 3.2 Training strategies
- 3.3 Workflow strategies
- 3.4 Discussion
- 4 Handling limited data access
- 4.1 Utilizing labeled data
- 4.2 Utilizing unlabeled data
- 4.3 Discussion
- 5 Multi-modal training
- 5.1 Correlated feature learning
- 5.2 Discussion
- 6 Summary and discussion
- 6.1 Summary of contributions
- 6.2 Clinical impact
- 6.3 Ethical considerations
- 6.4 Future outlook
- 6.5 Concluding remarks
- Bibliography
- II Appended papers.