Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
Description
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.
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Persons
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.
Content
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
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
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