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

ISBN-13: 9783030139711 / Angielski / Miękka / 2020 / 461 str.

Le Lu; Xiaosong Wang; Gustavo Carneiro

Spis treści

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

O autorze

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.

Blurb

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.