Deep Learning for Medical Image Analysis

S. Kevin Zhou, Hayit Greenspan, Dinggang Shen, S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

• Editorial: Academic Press
• Año: 2017
• ISBN:
  9780128104095
• Categoría: Informatica
• Categoría: Media digitali

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.

• Covers common research problems in medical image analysis and their challenges
• Describes deep learning methods and the theories behind approaches for medical image analysis
• Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc.
• Includes a Foreword written by Nicholas Ayache

Part I

Introduction

Outline

Chapter 1. An Introduction to Neural Networks and Deep Learning

Chapter 2. An Introduction to Deep Convolutional Neural Nets for Computer Vision

Chapter 1

An Introduction to Neural Networks and Deep Learning

Heung-Il Suk Korea University, Seoul, Republic of Korea

Abstract

Artificial neural networks, conceptually and structurally inspired by neural systems, are of great interest along with deep learning, thanks to their great successes in various fields including medical imaging analysis. In this chapter, we describe the fundamental concepts and ideas of (deep) neural networks and explain algorithmic advances to learn network parameters efficiently by avoiding overfitting. Specifically, this chapter focuses on introducing (i) feed-forward neural networks, (ii) gradient descent-based parameter optimization algorithms, (iii) different types of deep models, (iv) technical tricks for fast and robust training of deep models, and (v) open source deep learning frameworks for quick practice.

Keywords

Neural networks; Convolutional neural network; Deep learning; Deep belief network; Deep Boltzmann machine

Chapter Outline

1.1 Introduction

1.2 Feed-Forward Neural Networks

1.2.1 Perceptron

1.2.2 Multi-Layer Neural Network

1.2.3 Learning in Feed-Forward Neural Networks

1.3 Convolutional Neural Networks

1.3.1 Convolution and Pooling Layer

1.3.2 Computing Gradients

1.4 Deep Models

1.4.1 Vanishing Gradient Problem

1.4.2 Deep Neural Networks

1.4.2.1 Auto-Encoder

1.4.2.2 Stacked Auto-Encoder

1...