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Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics - Research Book

Deep Learning And Convolutional Neural Networks For Medical Imaging And Clinical Informatics - Research Book

Author(s) Name:  E Lu,Xiaosong Wang,Gustavo Carneiro,Lin Yang

About the Book:

   This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.
   The books chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.

Table of Contents

  • Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning
  • Deep Learning for Muscle Pathology Image Analysis
  • 2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scans
  • Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples
  • Unsupervised Domain Adaptation of ConvNets for Medical Image Segmentation via Adversarial Learning
  • Glaucoma Detection Based on Deep Learning Network in Fundus Image
  • Thoracic Disease Identification and Localization with Limited Supervision
  • Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI
  • Automatic Vertebra Labeling in Large-Scale Medical Images Using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
  • Anisotropic Hybrid Network for Cross-Dimension Transferable Feature Learning in 3D Medical Images
  • ISBN:  978-3-030-13969-8

    Publisher:  Springer

    Year of Publication:  2019

    Book Link:  Home Page Url