Designing with Machine Learning in Digital Pathology : : Augmenting Medical Specialists Through Interaction Design.

Designing with Machine Learning in Digital Pathology : : Augmenting Medical Specialists Through Interaction Design.

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Bibliographic Details
Superior document:Linköping Studies in Science and Technology. Dissertations Series ; v.2157
:
Lindvall, Martin.
Place / Publishing House:Linköping : : Linkopings Universitet,, 2021.
{copy}2021.
Year of Publication:2021
Edition:1st ed.
Language:English
Series:Linköping Studies in Science and Technology. Dissertations Series
Online Access:
Physical Description:1 online resource (130 pages)
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Table of Contents:
  • Intro
  • Abstract
  • Acknowledgments
  • List of Publications
  • Contents
  • I Comprehensive Summary
  • 1 Introduction
  • 1.1 Research Scope and Delimitations
  • 1.2 Thesis Overview
  • 2 Background
  • 2.1 Machine Learning Basics
  • 2.2 Digital Pathology
  • 2.3 Artificial Intelligence in Pathology
  • 3 Theoretical Framework
  • 3.1 Automation and Human Control
  • 3.2 Challenges Designing with ML
  • 3.3 AI-assisted Decision-making
  • 4 Research Approach
  • 4.1 Constructive Design Research
  • 4.2 Motivations
  • 4.3 From Design Experiments to Generative Knowledge
  • 4.4 Designing for Efficiency
  • 5 Paper summary
  • 6 Design Experiments
  • 6.1 Overview
  • 6.2 Exploring Proactive Training Data Collection (DROID)
  • 6.3 AI-assisted Annotation (TW)
  • 6.4 AI-assisted Visual Search (LGL)
  • 6.5 AI-assisted Quantification (PDL1)
  • 7 A Framework for Designing Human-Centred Machine Learning
  • 7.1 The Importance of Thoughtful Action
  • 7.2 Three Interconnected Activities
  • 7.3 The Impact of Gathering Training Data
  • 7.4 The Impact of Interaction Design
  • 7.5 The Impact of Model Development
  • 8 Conclusion and Discussion
  • 8.1 Summary of Contributions
  • 8.2 Why is Designing with ML Difficult?
  • 8.3 Power to the People? Reflections on Interactive Machine Learning
  • 8.4 The Role of Constructive Design Research
  • 8.5 In Conclusion
  • Bibliography
  • II Appended papers
  • 1 TissueWand, a Rapid Histopathology Annotation Tool.
  • 2 Rapid Assisted Visual Search: Supporting Digital Pathologists with Imperfect AI.
  • 3 From Machine Learning to Machine Teaching: The Importance of UX.
  • 4 Machine Learning as a Design Material: a Curated Collection of Exemplars for Visual Interaction.
  • 5 Verification Staircase: a Design Strategy for Actionable Explanations.
  • 6 Designing for the Long-Tail of Machine Learning.
  • 7 Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training.

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