Medical Data Analysis and Processing using Explainable Artificial Intelligence 9781032191126, 9781032191133, 9781003257721
The text presents concepts of explainable artificial intelligence (XAI) in solving real world biomedical and healthcare
167 104 2MB
English Pages 269 Year 2023
- Author / Uploaded
- Om Prakash Jena
- Mrutyunjaya Panda
- Utku Kose
Table of contents :
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Chapter 1 Explainable AI (XAI) Concepts and Theory
1.1 Introduction
1.2 Formal Definitions of Explainable Artificial Intelligence
1.3 The Working Mechanism of Explainable Artificial Intelligence: How Explainable Artificial Intelligence Generates Explanations
1.4 How Humans Reason (with Errors)
1.4.1 Dual-Process Model
1.4.2 Forward and Backward Reasoning
1.5 How Explainable Artificial Intelligence Support Reason and Solve Human Error Issue
1.5.1 Mitigate Representativeness Bias
1.5.2 Mitigate Availability Bias
1.5.3 Mitigate Anchoring Bias
1.5.4 Mitigate Confirmation Bias
1.6 Applications and Impact Areas of Explainable Artificial Intelligence
1.6.1 Threat Detection
1.6.2 Object Detection
1.6.3 Adversarial ML Prevention
1.6.4 Open Source Intelligence
1.6.5 Automated Medical Diagnosis
1.6.6 Autonomous Vehicles
1.7 Benefits of Explainable Artificial Intelligence
1.8 Research Challenges of Explainable Artificial Intelligence
1.9 Use Cases of Explainable Artificial Intelligence
1.9.1 Use Case of Explainable Artificial Intelligence: Social Media
1.9.2 Use Case of Explainable Artificial Intelligence: Healthcare
1.10 Limitations of Explainable Artificial Intelligence
1.11 Conclusion
References
Chapter 2 Utilizing Explainable Artificial Intelligence to Address Deep Learning in Biomedical Domain
2.1 Introduction: Background and Driving Forces
2.2 XAI Taxonomy
2.3 Review of State of Art
2.3.1 Methods Focused on Features
2.3.2 Global Methods
2.3.3 Concept Methods
2.3.4 Surrogate Methods
2.3.5 Local, Pixel-based Techniques
2.3.6 Human-centered Methods
2.4 Deep Learning – Reshaping Healthcare
2.4.1 Deep Learning Methods
2.4.2 Deep Learning Applications in Healthcare
2.5 Results
2.6 Benefits and Drawbacks of XAI Methods
2.6.1 Validity
2.6.2 Usability
2.6.3 Cost of Computation
2.6.4 Robustness
2.6.5 Fine-Tuning
2.6.6 Open Source
2.7 Conclusion
References
Chapter 3 Explainable Fuzzy Decision Tree for Medical Data Classification
3.1 Introduction
3.2 Literature Survey
3.3 Fuzzy Classification Problem
3.4 Induction of Fuzzy Decision Tree
3.4.1 Fuzzy c-means Clustering
3.4.2 Cluster Validity Indices and Optimality Condition (Narayanan et al., 2017)
3.4.3 Basics of Developing Fuzzy ID3
3.5 Case Study: Explainable FDT for HCV Medical Data
3.6 Conclusion and Future Work
References
Chapter 4 Statistical Algorithm for Change Point Detection in Multivariate Time Series of Medicine Data Based on Principles of Explainable Artificial Intelligence
4.1 Introduction
4.2 Detection of Change Points in Multivariate Time Series
4.3 Petunin's Ellipses and Ellipsoids
4.4 Numerical Experiments
4.4.1 Almost Non-overlapped Uniform Distributions with Different Locations
4.4.2 Uniform Distributions with Different Locations that Initially Are Strongly Overlapped, then Slightly Overlapped, and Finally Are Not Overlapped
4.4.3 Almost Non-overlapped Normal Distributions with Different Locations
4.4.4 Normal Distributions with the Same Location and Scales That are Gradually Begin to Differ
4.4.5 Normal Distributions with the Same Locations and Strongly Different Scales
4.4.6 Exponential Distributions with Different Parameters
4.4.7 Gamma-Distributions with the Same Location and Different Scales
4.4.8 Gamma-Distributions with Different Locations and the Same Scale
4.4.9 Gumbel Distributions with Different Locations and the Same Scale
4.4.10 Gumbel Distributions with the Same Location and Different Scales
4.4.11 Rayleigh Distributions with Different Scales
4.4.12 Laplace Distributions with Different Means and the Same Variance
4.4.13 Laplace Distributions with the Same Location and Different Scales
4.4.14 Logistic Distributions with Different Locations and the Same Scale
4.4.15 Logistic Distributions with the Same Location and Different Scales
4.4.16 Conclusion on Numerical Experiments
4.5 Quasi-real Experiments
4.5.1 Simulation of Tachycardia
4.5.2 Simulation of Coronavirus Pneumonia
4.5.3 Simulation of Cancer Lung
4.5.4 Simulation of Physical Activity
4.5.5 Simulation of Stress/Panic Attack
4.5.6 Conclusion on Quasi-Real Experiments
4.6 Conclusion
References
Chapter 5 XAI and Machine Learning for Cyber Security: A Systematic Review
5.1 Introduction to Explainable AI
5.2 Principles Followed by XAI Algorithm
5.3 Types of Explainability
5.4 Some Critical Applications of Explainability
5.5 Related Work
5.6 Historical Origins of the Need for Explainable AI
5.7 Taxonomy of MAP of Explainability Approaches
5.8 Challenges Posed by XAI
5.8.1 A Black-Box Attack on XAI in Cybersecurity
5.8.2 Manipulation of Adversarial Models to Deceive Neural Network Interpretations
5.8.3 Geometry is Responsible for the Manipulation of Explanations
5.8.4 Saliency Method’s Unreliability
5.8.5 Misleading Black-Box Explanations are Used to Manipulate User Trust
5.9 Various Suggested Solutions for XAI Security Challenges
5.9.1 Addressing Manipulation of User Trust Through Misleading Black Box Explanations
5.9.2 Improved Interpretability of DL
5.9.3 Heat-Map Explanations Defense Against Adversarial Cyber Attacks
5.9.4 Curvature Minimization
5.9.5 Weight Decay
5.9.6 Smoothing Activation Functions
5.10 Conclusion
Acknowledgment
References
Chapter 6 Classification and Regression Tree Modelling Approach to Predict the Number of Lymph Node Dissection among Endometrial Cancer Patients
6.1 Introduction
6.2 Data Source
6.3 Methods Used
6.3.1 Regression Tree
6.3.2 Optimal Threshold Value (Cut Off Point)
6.3.3 Regression Tree Algorithm
6.3.4 Validation of Models
6.4 Applications to EC Data
6.5 Discussion
6.6 Conclusion
References
Chapter 7 Automated Brain Tumor Analysis Using Deep Learning-Based Framework
7.1 Introduction
7.2 Related Works
7.3 Background
7.3.1 Autoencoders
7.3.2 Convolutional Autoencoders
7.3.3 Pre-trained Deep Classification Architectures
7.4 Proposed Methodology
7.4.1 Image Denoising
7.4.2 Tumor Detection and Tumor Grade Classification
7.4.3 Model Training
7.5 Result Analysis
7.5.1 Evaluation Metrics
7.5.2 Performance Evaluation
7.6 Conclusion
References
Chapter 8 A Robust Framework for Prediction of Diabetes Mellitus Using Machine Learning
8.1 Introduction
8.2 Background
8.3 Related Work
8.4 Conceptual Approach
8.5 Evaluation
8.6 Discussion
8.7 Conclusion
References
Chapter 9 Effective Feature Extraction for Early Recognition and Classification of Triple Modality Breast Cancer Images Using Logistic Regression Algorithm
9.1 Introduction
9.2 Symptoms of Breast Cancer
9.3 Need for Early Detection
9.4 Datasets Used
9.5 Classification of Medical Features from Three Modalities Using Logistic Regression Algorithm
9.5.1 Pre Processing
9.5.2 Segmentation
9.5.3 Feature Evaluation and Selecting Feature Vector
9.5.4 Classification
9.6 Results
9.6.1 Preprocessed US Images
9.6.2 Preprocessed Mammogram Images
9.7 Conclusion
References
Chapter 10 Machine Learning and Deep Learning Models Used to Detect Diabetic Retinopathy and Its Stages
10.1 Introduction
10.2 Conventional ML & DL Algorithms
10.2.1 ML – Support Vector Machine (ML – SVM)
10.2.2 ML – K_Nearest Neighbors (ML – KNN)
10.2.3 ML – Random Forest (ML – RF)
10.2.4 ML – Neural Networks (ML – NN)
10.2.5 Deep Learning
10.2.6 DL – Classic Neural Networks
10.2.7 DL – Convolutional Neural Networks (DL – CNN)
10.2.8 DL – LSTMNs (Long Short-Term Memory Networks)
10.2.9 DL – Recurrent Neural Networks (DL – RNN)
10.2.10 DL – Generative Adversarial Networks (DL – GAN)
10.2.11 DL – Reinforcement Learning
10.3 Retinal Image Datasets Used in DR Detection
10.4 DR Process Detection
10.4.1 Non-Proliferative_DR
10.4.2 Proliferative_DR
10.5 DR Lesion Segmentation
10.6 Performance Metrics
10.6.1 True Positive
10.6.2 True Negative
10.6.3 False Positive
10.6.4 False Negative
10.7 Conclusion
References
Chapter 11 Clinical Natural Language Processing Systems for Information Retrieval from Unstructured Medical Narratives
11.1 Introduction
11.1.1 Natural Language Understanding
11.1.2 Natural Language Generation
11.2 Stages of NLP
11.2.1 Phonological Analysis
11.2.2 Morphological and Lexical Analysis
11.2.3 Syntactic Analysis
11.2.4 Semantic Analysis
11.2.5 Discourse Integration
11.2.6 Pragmatic Analysis.
11.3 Applications and Techniques
11.3.1 Optical Character Recognition
11.3.2 Named Entity Recognition
11.3.3 Question Answering
11.3.4 Chatbots
11.3.5 Machine Translation
11.3.6 Sentiment Analysis
11.3.7 Topic Modeling
11.3.8 Automatic Text Summarization
11.3.9 Co-reference Resolution
11.3.10 Disease Prediction
11.3.11 Text Classification
11.3.12 Cognitive Assistant
11.3.13 Automatic Speech Recognition
11.4 NLP Systems in Health Care
11.5 Conclusion
References
Index
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