Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Description

Springer Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics by Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang

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. _x000D_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._x000D_ Table of contents : - _x000D_ Clinical Report Guided Multi-Sieving Deep Learning for Retinal Microaneurysm Detection_x000D_ Ling Dai, Ruogu Fang, Huating Li, Xuhong Hou, Bin Sheng, Qiang Wu and Weiping Jia_x000D_ _x000D_ _x000D_ _x000D_ Optic Disc and Cup Segmentation Based on Multi-label Deep Network for Fundus Glaucoma Screening_x000D_ Huazhu Fu, Jun Cheng, Yanwu Xu, Damon Wing Kee Wong, and Jiang Liu_x000D_ _x000D_ _x000D_ _x000D_ Thoracic Disease Identification and Localization with Limited Supervision_x000D_ Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, and Fei-Fei Li_x000D_ _x000D_ _x000D_ _x000D_ ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases_x000D_ X Wang, Y Peng, L Lu, Z Lu, M Bagheri, and RM Summers_x000D_ _x000D_ _x000D_ _x000D_ TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays_x000D_ Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, and Ronald Summers_x000D_ _x000D_ _x000D_ _x000D_ Deep Lesion Graph in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database_x000D_ Ke Yan, Xiaosong Wang,; Le Lu, Ling Zhang, Adam Harrison, HADI Bagheri, and Ronald Summers_x000D_ _x000D_ _x000D_ _x000D_ Deep Reinforcement Learning based Attention to Detect Breast Lesions from DCE-MRI_x000D_ Gabriel Maicas, Andrew Bradley, Jacinto Nascimento, Ian Reid, and Gustavo Carneiro_x000D_ _x000D_ _x000D_ _x000D_ Deep Convolutional Hashing for Low Dimensional Binary Embedding of Histopathological Images_x000D_ M. Sapkota, X. Shi, F. Xing, and L. Yang_x000D_ _x000D_ _x000D_ _x000D_ Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning_x000D_ J. Cai, L. Lu, F. Xing, and L. Yang_x000D_ _x000D_ _x000D_ _x000D_ Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation_x000D_ Y. Xie, Z. Zhang, M. Sapkota, and L. Yang_x000D_ _x000D_ _x000D_ _x000D_ Pancreas_x000D_ Alan Yuille_x000D_ _x000D_ _x000D_ _x000D_ Multi-Organ_x000D_ Alan Yuille_x000D_ _x000D_ _x000D_ _x000D_ Convolutional Invasion and Expansion Networks for Tumor Growth Prediction_x000D_ Ling Zhang, Le Lu, Ronald Summers, Electron Kebebew, and Jianhua Yao_x000D_ _x000D_ _x000D_ _x000D_ Cross-Modality Synthesis in Magnetic Resonance Imaging_x000D_ Yawen Huang, Ling Shao, and Alejandro F. Frangi_x000D_ _x000D_ _x000D_ _x000D_ Image Quality Assessment for Population Cardiac MRI_x000D_ Le Zhang, Marco Pereanez, and Alejandro F. Frangi_x000D_ _x000D_ _x000D_ _x000D_ Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss_x000D_ Qingsong Yang, Pingkun Yan, Yanbo Zhang, Hengyong Yu, Yongyi Shi, Xuanqin Mou, Mannudeep K Kalra, Yi Zhang, Ling Sun, and Ge Wang_x000D_ _x000D_ _x000D_ _x000D_ Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss_x000D_ Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, and Pheng-Ann Heng_x000D_ _x000D_ _x000D_ _x000D_ Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization_x000D_ Dong Yang, Tao Xiong, and Daguang Xu_x000D_ _x000D_ _x000D_ _x000D_ 3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes_x000D_ Siqi Liu and Daguang Xu_x000D_ _x000D_ _x000D_ _x000D_ Multi-Agent Learning for Robust Image Registration_x000D_ Shun Miao, Rui Liao, and Tommaso Mansi_x000D_ _x000D_ _x000D_ _x000D_ Deep Learning in Magnetic Resonance Imaging of Cardiac Function_x000D_ Dong Yang and Drimitri Metaxas_x000D_ _x000D_ _x000D_ _x000D_ Automatic Vertebra Labeling in Large-Scale Medical Images using Deep Image-to-Image Network with Message Passing and Sparsity Regularization_x000D_ Dong Yang, Tao Xiong, and Daguang Xu_x000D_ _x000D_ _x000D_ _x000D_ Deep Learning on Functional Connectivity of Brain: Are We There Yet?_x000D_ Harish Ravi Prakash, Arjun Watane, Sachin Jambawalikar, and Ulas Bagci_x000D_