Artificial Intelligence and Deep Learning in Pathology-1판
- 저 자 : Cohen
- 역 자 :
- 출판사 : Elsevier
- ISBN(13) : 9780323675383
- 발행일 : 2020-06-16 / 1판 / 250 페이지
- 상품코드 : 27650
- 적립금: 2,340원
- 정가: $99.99
Description:
Recent advances in computational algorithms along with the advent of whole slide imaging as a platform for embedding
artificial intelligence (AI) are transforming pattern recognition and image interpretation for diagnosis and
prognosis.
Yet most pathologists have just a passing knowledge of data mining machine learning and AI and little exposure to the
vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial
Intelligence and Deep Learning in Pathology with a team of experts Dr. Stanley Cohen covers the nuts and bolts of all
aspects of machine learning up to and including AI bringing familiarity and understanding to pathologists at all
levels of experience.
Key Features
avoiding complex mathematics whenever possible.
imaging
for 2D and 3D analysis, and general principles of image analysis and deep learning.
AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms,
identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more. 
Chapter 1. The evolution of machine learning: past, present, and future
Introduction
Rules-based versus machine learning: a deeper look
Varieties of machine learning
더보기
General aspects of machine learning
Deep learning and neural networks
The role of AI in pathology
Chapter 2. The basics of machine learning: strategies and techniques
Introduction
Shallow learning
The curse of dimensionality and principal component analysis
더보기
Deep learning and the artificial neural network
Overfitting and underfitting
Things to come
Chapter 3. Overview of advanced neural network architectures
Introduction
Network depth and residual connections
Autoencoders and unsupervised pretraining
더보기
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
Introduction
Life before machine learning
Multilabel classification
더보기
Multiple objects
Advances in multilabel classification
Graphical neural networks
Weakly supervised learning
Synthetic data
N-shot learning
One-class learning
General considerations
Summary and conclusions
Chapter 5. Dealing with data: strategies of preprocessing data
Introduction
Overview of preprocessing
Feature selection, extraction, and correction
더보기
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. Digital pathology as a platform for primary diagnosis and augmentation via deep
learning
Introduction
Digital imaging in pathology
Telepathology
더보기
Whole slide imaging
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
Whole slide imaging and primary diagnosis
Whole slide imaging and image analysis
Whole slide imaging and deep learning
Conclusions
Chapter 7. Applications of artificial intelligence for image enhancement in pathology
Introduction
Common machine learning tasks
Commonly used deep learning methodologies
더보기
Common training and testing practices
Deep learning for microscopy enhancement in histopathology
Deep learning for computationally aided diagnosis in histopathology
Future prospects
Chapter 8. Precision medicine in digital pathology via image analysis and machine learning
Introduction
Applications of image analysis and machine learning
Practical concepts and theory of machine learning
더보기
Image-based digital pathology
Regulatory concerns and considerations
Chapter 9. Artificial intelligence methods for predictive image-based grading of human cancers
Introduction
Tissue preparation and staining
Image acquisition
더보기
Stain normalization
Unmixing of immunofluorescence spectral images
Automated detection of tumor regions in whole-slide images
Image segmentation
Protein biomarker features
Morphological features for cancer grading and prognosis
Modeling
Ground truth data for AI-based features
Conclusion
Chapter 10. Artificial intelligence and the interplay between tumor and immunity
Introduction
Immune surveillance and immunotherapy
Identifying TILs with deep learning
더보기
Multiplex immunohistochemistry with digital pathology and deep learning
Vendor platforms
Conclusion
Chapter 11. Overview of the role of artificial intelligence in pathology: the computer as a pathology digital
assistant
Introduction
Computational pathology: background and philosophy
Machine learning tools in computational pathology: types of artificial intelligence
더보기
The need for human intelligence–artificial intelligence partnerships
Human transparent machine learning approaches
Image-based computational pathology
First fruits of computational pathology: the evolving digital assistant
Artificial intelligence and regulatory challenges
Educating machines–educating us: learning how to learn with machines