Big Data in Multimodal Medical Imaging

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

Table of Contents:
Big Data Applications in Lung Research. Artificial convolution neural network techniques and applications for big data of lung for nodule detection. Deep learning with non-medical training used for pathology identification in big data chest images. Unsupervised pre-training across image domains improves lung tissue classification in lung big data sets. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks in big data sets of CT Lungs. Big Data Applications in Colon Research. A comprehensive computer-aided polyp detection system for big data colonoscopy videos. Automatic polyp detection in big data colonoscopy videos using an ensemble of convolutional neural networks. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations in big data colonoscopy. Off-the-shelf convolutional neural network features for pulmonary nodule detection in big data computed tomography scans. Big Data Applications in Breast Cancer. Mitosis detection in big data breast cancer histology images with deep neural networks. Convolutional neural networks for mass lesion classification in big data mammography. Standard plane localization in fetal ultrasound via domain transferred deep neural networks in large ultrasound data sets. Unregistered multiview analysis with pre-trained deep learning models in large mammographic data sets. Big Data Applications in Brain Imaging. Brain tumor segmentation with deep neural networks using big data sets. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation in big data MRI images. Deep neural networks segment neuronal membranes in electron microscopy images. Alzheimer's Disease Diagnosis by Adaptation of 3D Convolutional Network in large MRI brain images. Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. Big Data Applications in Heart Imaging. Automating carotid intima-media thickness video interpretation with convolutional neural networks. Interleaved text/image deep mining on a very large-scale radiology database. Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition in big data sets. Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks in large MRI populations. Big Data Applications in Urology and Abdomen Imaging. A New NMF-Autoencoder Based CAD System for Early Diagnosis of Prostate Cancer by considering big data sets. Image-Based Computer-Aided Diagnosis for Early Diagnosis of Prostate Cancer in large data sets. Deep convolutional networks for pancreas segmentation in large scale CT imaging. A Promising Non-invasive CAD System for Kidney Function Assessment.

About the Author :
Ayman El-Baz, Ph.D., Professor, University Scholar, and Chair of Bioengineering Department at the University of Louisville, KY. Dr. El-Baz earned his bachelor's and master degrees in Electrical Engineering in 1997 and 2001. He earned his doctoral degrees in electrical engineering from the University of Louisville in 2006. In 2009, Dr. El-Baz was named a Coulter Fellow for his contribution in the biomedical translational research. Dr El-Baz has 15 years of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 300 technical articles (87 journals, 9 books, 39 book chapters, 144 refereed-conference papers, 74 abstracts published in proceedings, and 12 US patents). Jasjit S. Suri, an innovator, a visionary, a scientist, and an internationally-known world leader, has spent about 30 years in the field of biomedical engineering/sciences, software and hardware engineering and its management. During his career in biomedical industry/imaging, he has had an upstream growth and responsibilities from scientific Engineer, Scientist, Manager, Director R&D, Sr. Director, Vice President, Chief Technology Officer (CTO), CEO level positions in industries like Siemens Medical Systems, Philips Medical Systems, Fisher Imaging Corporation and Eigen Inc., Global Biomedical Technologies Inc., AtheroPoint™, respectively and managed unto a maximum of 50 to 100 people. He is currently the Chairman of Global Biomedical Technologies, Inc., CA, USA.