
Artificial Intelligence in Pathology
Principles and Applications
- 2nd Edition - November 15, 2024
- Imprint: Elsevier
- Editors: Chhavi Chauhan, Stanley Cohen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 3 5 9 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 8 3 2 - 5
Artificial Intelligence in Pathology: Principles and Applications provides a strong foundation of core artificial intelligence principles and their applications in the field of… Read more

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Request a sales quoteArtificial Intelligence in Pathology: Principles and Applications provides a strong foundation of core artificial intelligence principles and their applications in the field of digital pathology. This is a reference of current and emerging use of AI in digital pathology as well as the emerging utility of quantum artificial intelligence and neuromorphic computing in digital pathology. It is a must-have educational resource for lay public, researchers, academicians, practitioners, policymakers, key administrators, and vendors to stay current with the shifting landscapes within the emerging field of digital pathology. It is also of use to workers in other diagnostic imaging areas such as radiology.
This resource covers various aspects of the use of AI in pathology, including but not limited to the basic principles, advanced applications, challenges in the development, deployment, adoption, and scalability of AI-based models in pathology, the innumerous benefits of applying and integrating AI in the practice of pathology, ethical considerations for the safe adoption and deployment of AI in pathology.
- Discusses the evolution of machine learning in the field to provide a foundational background
- Addresses challenges in the development, deployment and regulation of AI in anatomic pathology
- Includes information on generative deep learning in digital pathology workflows
- Provides current tools and future perspectives
- Artificial Intelligence in Pathology
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Acknowledgments
- Part I: Principles
- Chapter 1 The evolution of machine learning: Past, present, and future
- Abstract
- Keywords
- Introduction
- Rules-based vs machine learning: A deeper look
- Varieties of machine learning
- General aspects of machine learning
- Deep learning and neural networks
- The role of AI in pathology
- Limitations of AI
- General aspects of AI
- References
- Chapter 2 The basics of machine learning: Strategies and techniques
- Abstract
- Keywords
- Introduction
- Shallow learning
- Geometric (distance-based) models
- The K-means algorithm (KM)
- Probabilistic models
- Decision trees and random forests
- The curse of dimensionality and PCA
- Deep learning and the ANN
- Neuroscience 101
- The rise of the machines
- The basic ANN
- The weights in an ANN
- Learning from examples: Backprop and stochastic gradient descent
- Convolutional neural networks
- Overview
- Detailed explanation
- Overfitting and underfitting
- Things to come
- References
- Chapter 3 Overview of advanced neural network architectures
- Abstract
- Keywords
- Introduction
- Network depth and residual connections
- Autoencoders and unsupervised pretraining
- Transfer learning
- Generative models and generative adversarial networks
- Recurrent neural networks
- Reinforcement learning
- Ensembles
- Genetic algorithms
- References
- Chapter 4 Complexity in the use of artificial intelligence in anatomic pathology
- Abstract
- Keywords
- Introduction
- Life before machine learning
- Multilabel classification
- Single object detection
- Multiple objects
- Advances in multilabel classification
- Graphical neural networks
- Capsule networks
- Weakly supervised learning
- Synthetic data
- N-shot learning
- One-class learning
- Risk analysis
- General considerations
- Summary and conclusions
- References
- Chapter 5 Dealing with data: Strategies of preprocessing data
- Abstract
- Keywords
- Introduction
- Overview of preprocessing
- Feature selection, extraction, and correction
- Feature transformation, standardization, and normalization
- Feature engineering
- Mathematical approaches to dimensional reduction
- Dimensional reduction in deep learning
- Imperfect class separation in the training set
- Fairness and bias in machine learning
- Summary
- References
- Chapter 6 Artificial intelligence in pathology: Easing the burden of annotation
- Abstract
- Keywords
- Introduction
- Artificial intelligence 101
- The human in the loop: Harvesting usable data
- Reducing the need for annotated data
- Overview of unsupervised pretraining
- Transfer learning
- Unsupervised pretraining via clustering
- One class learning
- Unsupervised pretraining via autoencoders
- Unsupervised pretraining via generative adversarial networks
- Reinforcement learning (RL)
- Self-supervised learning
- Zero shot learning
- Drowning in data: Quantum computing to the rescue
- Summary and overview
- References
- Chapter 7 Digital pathology as a platform for primary diagnosis and augmentation via deep learning
- Abstract
- Keywords
- Introduction
- Digital imaging in pathology
- Telepathology
- Whole slide imaging (WSI)
- Whole slide image viewers
- Whole slide image data and workflow management
- Selection criteria for a whole slide scanner
- Evolution of whole slide imaging systems
- Infrastructure requirements and checklist for rolling out high-throughput whole slide imaging workflow solution
- WSI and primary diagnosis
- WSI and image analysis
- WSI and deep learning
- Conclusions
- References
- Chapter 8 Artificial intelligence model development, deployment, and regulatory challenges in anatomic pathology
- Abstract
- Keywords
- Introduction
- Development challenges
- Problem identification
- Dataset curation and annotation
- Model development and training
- Hardware and cost
- Deployment challenges
- Pathologist buy-in and transitioning to a digital workflow
- IT infrastructure: Cloud computing vs. on-premises solutions
- Lack of pathologist’s experience with AI
- What is the right evidence standard for AI to be embedded in practice?
- What is required for clinical validation prior to using AI for diagnostic purposes?
- What is the ideal workflow when implementing AI in clinical practice?
- What model do pathology laboratories use to pay AI vendors?
- What is the business use case for deploying AI?
- Should residents or fellows be allowed to use AI, or is this “cheating?”
- Regulatory challenges
- FDA
- European Union Conformité Européenne
- CMS/CLIA
- Conclusion
- References
- Chapter 9 Ethics of AI in pathology: Current paradigms and emerging issues
- Abstract
- Keywords
- Disclosures
- Introduction
- Ethical AI study designs in pathology
- Inclusive AI design and bias
- Race in ethical AI design
- Stakeholder concerns: Consent and awareness
- Risks of AI in pathology and to pathologists—Real or imagined?
- Underestimating the risks of AI to pathology
- Overestimating the risks of AI to pathology
- Institutional frameworks to enable ethical AI in pathology
- Transparency
- Accountability
- Governance
- Recent developments in the use of AI in pathology
- Conclusions
- References
- Part II: Applications
- Chapter 10 Applications of artificial intelligence for image enhancement in pathology
- Abstract
- Keywords
- Acknowledgement
- Introduction
- Common machine learning tasks
- Classification
- Segmentation
- Image translation and style transfer
- Commonly used deep learning methodologies
- Convolutional neural networks
- U-nets
- Generative adversarial networks and their variants
- Common training and testing practices
- Dataset preparation and preprocessing
- Loss functions
- Metrics
- Deep learning for microscopy enhancement in histopathology
- Stain color normalization
- Mode switching
- In silico labeling
- Super-resolution, extended depth-of-field, and denoising
- Deep learning for computationally aided diagnosis in histopathology
- A rationale for AI-assisted imaging and interpretation
- Approaches to rapid histology interpretations
- Future prospects
- References
- Chapter 11 Foundation models and information retrieval in digital pathology
- Abstract
- Keywords
- Introduction
- Information retrieval
- Image search
- Validation of image search methods
- Large deep models
- Foundation models
- Generative AI
- Information retrieval and foundation models
- Conclusions
- References
- Chapter 12 Precision medicine in digital pathology via image analysis and machine learning
- Abstract
- Keywords
- Acknowledgments
- Introduction
- Precision medicine
- Digital pathology
- Applications of image analysis and machine learning
- Knowledge-driven image analysis
- Machine learning for image segmentation
- Deep learning for image segmentation
- Spatial resolution
- Machine learning on extracted data
- Beyond augmentation
- Practical concepts and theory of machine learning
- Machine learning and digital pathology
- Common techniques
- Supervised learning
- Unsupervised learning
- Image-based digital pathology
- Conventional approaches to image analysis
- Deep learning on images
- Regulatory concerns and considerations
- References
- Chapter 13 Generative deep learning in digital pathology
- Abstract
- Keywords
- Acknowledgments
- Introduction
- Deep generative models
- Generative models in the digital pathology pipeline
- Color and intensity normalization
- Data adaptation
- Data synthesis
- Future directions
- Conclusion
- References
- Chapter 14 Artificial intelligence methods for predictive image-based grading of human cancers
- Abstract
- Keywords
- Introduction
- Tissue preparation and staining
- Image acquisition
- Stain normalization
- Unmixing of immunofluorescence spectral images
- Automated detection of tumor regions in whole-slide images
- Localization of diagnostically relevant regions of interest in whole-slide images
- Tumor detection
- Image segmentation
- Nuclear and epithelial segmentation in IF images
- Nuclei detection and segmentation in H&E images
- Epithelial segmentation in H&E images
- Mitotic figure detection
- Ring segmentation
- Protein biomarker features
- Morphological features for cancer grading and prognosis
- Modeling
- Cox proportional hazards model
- Neural networks
- Decision trees and random forests
- SVM-based methods: Survival-SVM, SVCR, and SVRc
- Feature selection tools
- Ground truth data for AI-based features
- Conclusion
- References
- Chapter 15 Artificial intelligence and the interplay between cancer and immunity
- Abstract
- Keywords
- Introduction
- Immune surveillance and immunotherapy
- Identifying TILs with deep learning
- Spatial cancer biology with Pathomics, immunohistochemistry, and immunofluorescence
- Conclusion
- References
- Chapter 16 Overview of the role of artificial intelligence in pathology: The computer as a pathology digital assistant
- Abstract
- Keywords
- Introduction
- Computational pathology: Background and philosophy
- The current state of diagnostics in pathology and the evolving computational opportunities: “why now?”
- Digital pathology versus computational pathology
- Data on scale
- Machine learning tools in computational pathology: Types of artificial intelligence
- The need for human intelligence-artificial intelligence partnerships
- Human-transparent machine learning approaches
- Explainable artificial intelligence
- Cognitive artificial intelligence
- Human-in-the-loop
- One-shot learning
- Image-based computational pathology
- Core premise of image analytics: What is a high-resolution image?
- The targets of image-based calculations
- First fruits of computational pathology: The evolving digital assistant
- The digital assistant for quality control
- The digital assistant for histological object segmentation
- The digital assistant in immunohistochemistry
- The digital assistant in tissue classification
- The digital assistant in finding metastases
- The digital assistant in predictive modeling and precision medicine
- The digital assistant for anatomical simulation learning
- The digital assistant for image-omics data fusion
- Artificial intelligence and regulatory challenges
- Educating machines-educating us: Learning how to learn with machines
- References
- Chapter 17 Overview and coda: The future of AI
- Abstract
- Keywords
- Introduction
- Transformers and attention
- Neuromorphic computing
- Quantum computing
- Summary and conclusions
- References
- Index
- Edition: 2
- Published: November 15, 2024
- Imprint: Elsevier
- No. of pages: 500
- Language: English
- Paperback ISBN: 9780323953597
- eBook ISBN: 9780323958325
CC
Chhavi Chauhan
SC