Artificial intelligence in pathology : principles and applications / edited by Chhavi Chauhan, Stanley Cohen.

Format:

Book

Contributor:

Chauhan, Chhavi, editor.

Cohen, Stanley, editor.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Physical Description:

1 online resource (486 pages)

Edition:

Second edition.

Place of Publication:

Amsterdam, Netherlands : Elsevier, [2025]

Summary:

Artificial Intelligence in Pathology: Principles and Applications provides a strong foundation of core artificial intelligence principles and their applications in the field of digital pathology.

Contents:

Intro

Artificial Intelligence in Pathology: Principles and Applications

Copyright

Contents

Contributors

Preface

Acknowledgments

Part I: Principles

Chapter 1: The evolution of machine learning: Past, present, and future

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

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

Chapter 3: Overview of advanced neural network architectures

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

Chapter 4: Complexity in the use of artificial intelligence in anatomic pathology

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

Chapter 5: Dealing with data: Strategies of preprocessing data

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

Chapter 6: Artificial intelligence in pathology: Easing the burden of annotation

Artificial intelligence 101

The human in the loop: Harvesting usable data

Reducing the need for annotated data

Overview of unsupervised pretraining

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

Chapter 7: Digital pathology as a platform for primary diagnosis and augmentation via deep learning

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

Chapter 8: Artificial intelligence model development, deployment, and regulatory challenges in anatomic pathology

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 pathologists 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

Funding source

Chapter 9: Ethics of AI in pathology: Current paradigms and emerging issues

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

Acknowledgment

Part II: Applications

Chapter 10: Applications of artificial intelligence for image enhancement in pathology

Common machine learning tasks

Classification

Segmentation

Image translation and style transfer

Commonly used deep learning methodologies

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

Acknowledgement

Chapter 11: Foundation models and information retrieval in digital pathology

Information retrieval

Image search

Validation of image search methods

Large deep models

Foundation models

Generative AI

Information retrieval and foundation models

Chapter 12: Precision medicine in digital pathology via image analysis and machine learning

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

Naïve Bayes assumption-based methods

Logistic regression-based methods

Support vector-based methods

Nonparametric, k-nearest neighbor-based methods

Random forests

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

Deep generative models

Generative models in the digital pathology pipeline

Color and intensity normalization

Stain-style transfer

Pix2Pix-base image-to-image translation

CycleGAN-based image-to-image translation

Data adaptation

Data synthesis

Future directions

Chapter 14: Artificial intelligence methods for predictive image-based grading of human cancers

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&amp

E images

Epithelial segmentation in H&amp

Mitotic figure detection

Ring segmentation

Protein biomarker features

Morphological features for cancer grading and prognosis

Modeling

Cox proportional hazards model

Neural networks

SVM-based methods: Survival-SVM, SVCR, and SVRc

Feature selection tools

Ground truth data for AI-based features

Chapter 15: Artificial intelligence and the interplay between cancer and immunity

Immune surveillance and immunotherapy

Identifying TILs with deep learning

Spatial cancer biology with Pathomics, immunohistochemistry, and immunofluorescence

Chapter 16: Overview of the role of artificial intelligence in pathology: The computer as a pathology digital assistan

Computational pathology: Background and philosophy.

The current state of diagnostics in pathology and the evolving computational opportunities: ``why now?´´.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9780323958325

032395832X

OCLC:

1474241788