Deep Learning Models for Medical Imaging Books

Click Get Book Button To Download or read online Deep Learning Models for Medical Imaging books, Available in PDF, ePub, Tuebl and Kindle. This site is like a library, Use search box in the widget to get ebook that you want.

Deep Learning Models for Medical Imaging


Deep Learning Models for Medical Imaging
  • Author : K.C. Santosh
  • Publisher : Academic Press
  • Release : 2021-01-15
  • ISBN : 0128235047
  • Language : En, Es, Fr & De
GET BOOK

Deep Learning Models for Medical Imaging is suitable for computer science, medical imaging and biomedical engineering researchers and students who need up-to-date deep learning tools to apply to medical image analysis problems. The book presents deep learning concepts and modeling as applied to medical imaging and/or healthcare, using two different real-world case studies, providing complete implementation (via GitHub) of both standard (e.g. LeNet, Alexnet, VGGNet, ResNet and InceptionNet) and recent models (Mobile net and squeeze-and excitation net). Provides a step-by-step approach to develop deep learning models Presents case studies showing end-to-end implementation Includes codes provided in GitHub

Deep Learning for Medical Image Analysis


Deep Learning for Medical Image Analysis
  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Release : 2017-01-18
  • ISBN : 9780128104095
  • Language : En, Es, Fr & De
GET BOOK

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 challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Machine Learning and Medical Imaging


Machine Learning and Medical Imaging
  • Author : Guorong Wu
  • Publisher : Academic Press
  • Release : 2016-08-11
  • ISBN : 9780128041147
  • Language : En, Es, Fr & De
GET BOOK

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Deep Learning for Medical Image Analysis


Deep Learning for Medical Image Analysis
  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Release : 2017-01-30
  • ISBN : 0128104082
  • Language : En, Es, Fr & De
GET BOOK

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 challenges Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Includes a Foreword written by Nicholas Ayache

Deep Learning Applications in Medical Imaging


Deep Learning Applications in Medical Imaging
  • Author : Sanjay Saxena
  • Publisher :
  • Release : 2020-08
  • ISBN : 1799850714
  • Language : En, Es, Fr & De
GET BOOK

Before the modern age of medicine, the chance of surviving a terminal disease such as cancer was minimal at best. After embracing the age of computer-aided medical analysis technologies, however, detecting and preventing individuals from contracting a variety of life-threatening diseases has led to a greater survival percentage and increased the development of algorithmic technologies in healthcare. Deep Learning Applications in Medical Imaging is a pivotal reference source that provides vital research on the application of generating pictorial depictions of the interior of a body for medical intervention and clinical analysis. While highlighting topics such as artificial neural networks, disease prediction, and healthcare analysis, this publication explores image acquisition and pattern recognition as well as the methods of treatment and care. This book is ideally designed for diagnosticians, medical imaging specialists, healthcare professionals, physicians, medical researchers, academicians, and students.