Deep Learners and Deep Learner Descriptors for Medical Applications

Deep Learners and Deep Learner Descriptors for Medical Applications
  • eBook:
    Deep Learners and Deep Learner Descriptors for Medical Applications
  • Author:
    Loris Nanni, Sheryl Brahnam, Rick Brattin, Stefano Ghidoni, Lakhmi C. Jain
  • Edition:
    1st ed. 2020 Edition
  • Categories:
  • Data:
    June 29, 2020
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    295 pages
  • Format:
    PDF, ePUB

Book Description
This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized:
1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data);
2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine;
3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets;
4) by fusing different deep learner architectures; and
5) by combining the above methods to generate a variety of more elaborate ensembles.
This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects. 


1. An Introduction to Deep Learners and Deep Learner Descriptors for Medical Applications

Part I - Deep Features and Their Fusion
2. Feature Learning to Automatically Assess Radiographic Knee Osteoarthritis Severity
3. Classification of Tissue Regions in Histopathological Images: Comparison Between Pre-trained Convolutional Neural Networks and Local Binary Patterns Variants
4. Ensemble of Handcrafted and Deep Learned Features for Cervical Cell Classification
5. Deep Unsupervised Representation Learning for Audio-Based Medical Applications

Part II - Augmentation
6. Data Augmentation in Training Deep Learning Models for Medical Image Analysis

Part III - Medical Applications and Reviews
7. Application of Convolutional Neural Networks in Gastrointestinal and Liver Cancer Images: A Systematic Review
8. Supervised CNN Strategies for Optical Image Segmentation and Classification in Interventional Medicine
9. Convolutional Neural Networks for 3D Protein Classification

Part IV - Ethical Considerations
10. From Artificial Intelligence to Deep Learning in Bio-medical Applications

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