Big Data in Multimodal Medical Imaging
暫譯: 多模態醫學影像中的大數據

有迫切的需求來開發和整合新的統計、數學、視覺化和計算模型，以分析大數據，從中提取有用的信息，幫助臨床醫生準確診斷和治療病人。本書的主要重點是回顧和總結最先進的大數據和深度學習方法，以分析和整合多種數據類型，創建決策矩陣，幫助臨床醫生早期診斷和識別高風險的人類疾病和障礙。領先的研究者將貢獻原創研究書章，分析解決這些重要問題的努力。

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(TM), respectively and managed unto a maximum of 50 to 100 people. He is currently the Chairman of Global Biomedical Technologies, Inc., CA, USA.

Ayman El-Baz, Ph.D.，路易斯維爾大學生物工程系教授、學者及系主任。El-Baz博士於1997年和2001年分別獲得電機工程學士及碩士學位，並於2006年在路易斯維爾大學獲得電機工程博士學位。2009年，El-Baz博士因其在生物醫學轉譯研究方面的貢獻而被授予Coulter Fellow。El-Baz博士在生物影像建模和非侵入式電腦輔助診斷系統領域擁有15年的實務經驗。他已發表或共同撰寫超過300篇技術文章（87篇期刊、9本書、39章書籍、144篇經審查的會議論文、74篇會議摘要及12項美國專利）。

Jasjit S. Suri，一位創新者、願景家、科學家及國際知名的世界領袖，在生物醫學工程/科學、軟體和硬體工程及其管理領域工作了約30年。在他的生物醫學產業/影像職業生涯中，他從科學工程師、科學家、經理、研發總監、高級總監、副總裁、首席技術官（CTO）到執行長等職位，逐步提升並承擔責任，曾在西門子醫療系統、飛利浦醫療系統、Fisher Imaging Corporation、Eigen Inc.、Global Biomedical Technologies Inc.及AtheroPoint(TM)等公司工作，管理人數最多可達50至100人。目前，他是美國加州Global Biomedical Technologies, Inc.的董事長。

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.

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.