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    Artificial Intelligence and Deep Learning in Pathology-1판
Artificial Intelligence a
       판매가 : 125,000112,500
       적립금 : 2,250
       저   자 : Cohen
       역   자 :
       출판사 : Elsevier
    ISBN(13) : 9780323675383
       발행일 : 2020-06-16  /   1판   /   250 페이지
       상품코드 : 27650
       수 량 :
       

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
  • Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and
    avoiding complex mathematics whenever possible.
  • Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide
    imaging
    for 2D and 3D analysis, and general principles of image analysis and deep learning.
  • Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs,
    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
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