Overview

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 book’s 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.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.  

Full Product Details

Author:   Le Lu ,  Xiaosong Wang ,  Gustavo Carneiro ,  Lin Yang
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2019
Weight:   0.724kg
ISBN:  

9783030139711

ISBN 10:   3030139719
Pages:   461
Publication Date:   01 October 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Clinical Report Guided Multi-Sieving Deep Learning for Retinal Microaneurysm Detection Ling Dai, Ruogu Fang, Huating Li, Xuhong Hou, Bin Sheng, Qiang Wu and Weiping Jia Optic Disc and Cup Segmentation Based on Multi-label Deep Network for Fundus Glaucoma Screening Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, and Jiang Liu Thoracic Disease Identification and Localization with Limited Supervision Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, and Fei-Fei Li ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases X Wang, Y Peng, L Lu, Z Lu, M Bagheri, and RM Summers TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, and Ronald Summers Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database Ke Yan, Xiaosong Wang,; Le Lu,  Ling Zhang,  Adam Harrison,  HADI Bagheri,  and Ronald Summers Deep Reinforcement Learning based Attention to Detect Breast Lesions from DCE-MRI Gabriel Maicas, Andrew Bradley, Jacinto Nascimento, Ian Reid, and Gustavo Carneiro Deep Convolutional Hashing for Low Dimensional Binary Embedding of Histopathological Images M. Sapkota, X. Shi, F. Xing, and L. Yang Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning J. Cai, L. Lu, F. Xing, and L. Yang Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation Y. Xie, Z. Zhang, M. Sapkota, and L. Yang Pancreas Alan Yuille Multi-Organ Alan Yuille Convolutional Invasion and Expansion Networks for Tumor Growth Prediction Ling Zhang, Le Lu, Ronald Summers, Electron Kebebew, and Jianhua Yao Cross-Modality Synthesis in Magnetic Resonance Imaging Yawen Huang, Ling Shao,  and Alejandro F. Frangi Image Quality Assessment for Population Cardiac MRI Le Zhang, Marco Pereañez,    and Alejandro F. Frangi Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou, Mannudeep K Kalra, Yi Zhang, Ling Sun, and Ge Wang Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, and Pheng-Ann Heng Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization Dong Yang, Tao Xiong, and Daguang Xu 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes Siqi Liu and Daguang Xu Multi-Agent Learning for Robust Image Registration Shun Miao, Rui Liao, and Tommaso Mansi Deep Learning in Magnetic Resonance Imaging of Cardiac Function Dong Yang and Drimitri Metaxas Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization Dong Yang, Tao Xiong, and Daguang Xu Deep Learning on Functional Connectivity of Brain: Are We There Yet? Harish Ravi Prakash, Arjun Watane, Sachin Jambawalikar, and Ulas Bagci

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Author Information

Dr. Le Lu is the Director of Ping An Technology US Research Labs, and an adjunct faculty member at Johns Hopkins University, USA. Dr. Xiaosong Wang is a Senior Applied Research Scientist at Nvidia Corp., USA. Dr. Gustavo Carneiro is an Associate Professor at the University of Adelaide, Australia. Dr. Lin Yang is an Associate Professor at the University of Florida, USA.

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