Deep Learning for Medical Image Analysis

S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

• Publisher: Academic Press
• Year: 2017
• eBook ISBN: 9780128104095
• Topic: Computer Science
• Subtopic: Business Intelligence
• Index: Computer Science

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...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• About the Editors
• Foreword
• Part I: Introduction
• Chapter 1: An Introduction to Neural Networks and Deep Learning
• Chapter 2: An Introduction to Deep Convolutional Neural Nets for Computer Vision
• Part II: Medical Image Detection and Recognition
• Chapter 3: Efficient Medical Image Parsing
• Chapter 4: Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition
• Chapter 5: Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks
• Chapter 6: Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images
• Chapter 7: Deep Voting and Structured Regression for Microscopy Image Analysis
• Part III: Medical Image Segmentation
• Chapter 8: Deep Learning Tissue Segmentation in Cardiac Histopathology Images
• Chapter 9: Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching
• Chapter 10: Characterization of Errors in Deep Learning-Based Brain MRI Segmentation
• Part IV: Medical Image Registration
• Chapter 11: Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning
• Chapter 12: Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration
• Part V: Computer-Aided Diagnosis and Disease Quantification
• Chapter 13: Chest Radiograph Pathology Categorization via Transfer Learning
• Chapter 14: Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions
• Chapter 15: Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease
• Part VI: Others
• Chapter 16: Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis
• Chapter 17: Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning
• Index