Deep Learning for Digital Pathology in Limited Data Scenarios.

• Superior document: Linköping Studies in Science and Technology. Licentiate Thesis Series ; v.2253
• Author: Stacke, Karin.
• Place / Publishing House: Linköping : : Linkopings Universitet,, 2022.; {copy}2022.
• Year of Publication: 2022
• Edition: 1st ed.
• Language: English
• Series: Linköping Studies in Science and Technology. Licentiate Thesis Series
• Physical Description: 1 online resource (85 pages)

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.