Deep Learning for Medical Image Analysis

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Covers common research problems in medical image analysis and their challengesDescribes deep learning methods and the theories behind approaches for medical image analysisTeaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. INDICE: Section I: Basics of Neural Networks and Deep Learning An introduction to neural network and deep learning (covering CNN, RNN, RBM, Autoencoders) (Heung-Il Suk, Korea University) An introduction to deep reinforcement learning Engineering issues and software packages for NN and DL A literature review of DL for MIA (Hayit Greenspan, Dinggang Shen, and S. Kevin Zhou) Section II: Deep Learning Algorithms for Detection Anatomy detection using marginal space deep learning (Bogdan Georgescu, Yefeng Zheng, Dorin, Siemens, USA) Deep neural networks segment neuronal membranes in electron microscopy images (D Ciresan, A Giusti, LM Gambardella, J Schmidhube, The Swiss AI Lab IDSIA, Switzerland ) Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection (Anant Madabushi, CWRU, USA) Body part recognition using multi-stage deep learning (Yiqiang Zhan, Sean Zhou, Siemens; Metaxes, Rutgers University, USA) An ensemble of CNNs for polyp detection using spatio-temporal information (Nima Tajbakhsh (ASU), Suryakanth R. Gurudu (Mayo Clinic, and Jianming Liang (ASU), USA) Detection of fetal ultrasound standard plane and intervertebral discs (Hao Chen, Pheng-Ann Heng, CUHK, Hong Kong) Section III: Deep Learning Algorithms for Image Segmentation and Registration U-net: Convolutional networks in biomedical image segmentation (Olaf Ronneberger, Philipp Fischer, and Thomas Brox, University of Freiburg, Germany) Contextual NN for medical image detection and segmentation & Mitosis detection (Hao Chen, Pheng-Ann Heng, CUHK, Hong Kong. Winner of 4 MICCAI 2015 challenges) Deep Organ and 2.5D representation (Holger Roth, Ron Summers, et al. NIH, USA) Deformable MR prostate segmentation using deep learning and sparse patch matching (Yanrong Guo, Yaozong Gao, Dinggang Shen, University of North Carolina at Chapel Hill, USA) Scalable high performance image registration framework by unsupervised deep feature representation learning (Guorong Wu and Dinggang Shen, BRIC and Department of Radiology, University of North Carolina at Chapel Hill, USA) Section IV: Deep Learning Algorithms for Computer-Aided Diagnosis Transfer deep learning for chest X-ray (Hayit Greenspan, Israel) Deep learning for breast CAD (Gustavo Carnerio, Australia, Navab, Nassir; TU Munich, Chair for Computer Aided Medical Procedures) Deep learning for Lung and chest / CT (Ginneken, Bram; Radboud University Medical Center, Netherland) Breast/ Mammograph/ risk scoring using deep learning (Nielsen, Mads; University of Copenhagen, Denmark) Randomized denoising autoencoders for smaller and efficient imaging based AD clinical trials (V. K. Ithapu, V. Singh, O. Okonkwo, S. C. Johnson, University of Wisconsin, Madison, USA) Computer-aided classification of lung nodules on computed tomography images via deep learning technique (Kai-Lung Hua, et al., National Taiwan University of Science and Technology, Taipei, Taiwan) Using deep learning to detect small intestine disorders (Prof. Petia Radeva, Universitat de Barcelona, Spain) Section V: Others Image synthesis using deep network (Hien Nguyen, S. Kevin Zhou, Siemens, USA) Text/Image mining on a large-scale radiology image database (Hoo-Chang Shin, Ron Summers, et al., NIH, USA) Reinforcement learning for medical image analysis (Tommaso Mansi, Bogdan Georgescu, Dorin, Siemens) Microscopy cell counting with fully convolutional regression networks (Oxford, Alison, Zisserman, Oxford University, UK) Deep voting and beyond classification for microscopy image analysis (Lin Yang et al., University of Florida, USA)

• ISBN: 978-0-12-810408-8
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 625
• Fecha Publicación: 01/02/2017
• Nº Volúmenes: 1
• Idioma: Inglés