Deep Learning Models for Medical Imaging

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  • Author : K.C. Santosh
  • Publisher : Academic Press
  • Pages : 180 pages
  • ISBN : 9780128235041
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Deep Learning Models for Medical Imaging

Download or Read online Deep Learning Models for Medical Imaging full in PDF, ePub and kindle. this book written by K.C. Santosh and published by Academic Press which was released on 15 January 2021 with total page 180 pages. We cannot guarantee that Deep Learning Models for Medical Imaging book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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 Models for Medical Imaging

Deep Learning Models for Medical Imaging
  • Author : K.C. Santosh,Nibaran Das,Swarnendu Ghosh
  • Publisher : Academic Press
  • Release : 15 January 2021
GET THIS BOOK Deep Learning Models for Medical Imaging

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

Deep Learning in Healthcare

Deep Learning in Healthcare
  • Author : Yen-Wei Chen,Lakhmi C. Jain
  • Publisher : Springer Nature
  • Release : 18 November 2019
GET THIS BOOK Deep Learning in Healthcare

This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output,

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis
  • Author : Gobert Lee,Hiroshi Fujita
  • Publisher : Springer Nature
  • Release : 06 February 2020
GET THIS BOOK Deep Learning in Medical Image Analysis

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Guorong Wu,Daoqiang Zhang,Luping Zhou
  • Publisher : Springer
  • Release : 05 September 2014
GET THIS BOOK Machine Learning in Medical Imaging

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, in Cambridge, MA, USA, in September 2014. The 40 contributions included in this volume were carefully reviewed and selected from 70 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Qian Wang,Yinghuan Shi,Heung-Il Suk,Kenji Suzuki
  • Publisher : Springer
  • Release : 06 September 2017
GET THIS BOOK Machine Learning in Medical Imaging

This book constitutes the refereed proceedings of the 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017, held in conjunction with MICCAI 2017, in Quebec City, QC, Canada, in September 2017. The 44 full papers presented in this volume were carefully reviewed and selected from 63 submissions. The main aim of this workshop is to help advance scientific research within the broad field of machine learning in medical imaging. The workshop focuses on major trends and challenges in this area, and presents works

Deep Neural Networks for Multimodal Imaging and Biomedical Applications

Deep Neural Networks for Multimodal Imaging and Biomedical Applications
  • Author : Suresh, Annamalai,Udendhran, R.,Vimal, S.
  • Publisher : IGI Global
  • Release : 26 June 2020
GET THIS BOOK Deep Neural Networks for Multimodal Imaging and Biomedical Applications

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the

Medical Image Computing and Computer Assisted Intervention MICCAI 2019

Medical Image Computing and Computer Assisted Intervention     MICCAI 2019
  • Author : Dinggang Shen,Tianming Liu,Terry M. Peters,Lawrence H. Staib,Caroline Essert,Sean Zhou,Pew-Thian Yap,Ali Khan
  • Publisher : Springer Nature
  • Release : 07 December 2019
GET THIS BOOK Medical Image Computing and Computer Assisted Intervention MICCAI 2019

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage

Deep Learning for Data Analytics

Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release : 29 May 2020
GET THIS BOOK Deep Learning for Data Analytics

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Heung-Il Suk,Mingxia Liu,Pingkun Yan,Chunfeng Lian
  • Publisher : Springer Nature
  • Release : 09 December 2019
GET THIS BOOK Machine Learning in Medical Imaging

This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning,

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Deep Learning and Convolutional Neural Networks for Medical Image Computing
  • Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
  • Publisher : Springer
  • Release : 12 July 2017
GET THIS BOOK Deep Learning and Convolutional Neural Networks for Medical Image Computing

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research

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

Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
  • Author : Le Lu,Xiaosong Wang,Gustavo Carneiro,Lin Yang
  • Publisher : Springer Nature
  • Release : 19 September 2019
GET THIS BOOK Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

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

Deep Learning with PyTorch

Deep Learning with PyTorch
  • Author : Eli Stevens,Luca Antiga,Thomas Viehmann
  • Publisher : Manning Publications
  • Release : 04 August 2020
GET THIS BOOK Deep Learning with PyTorch

Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new

Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments

Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments
  • Author : Alex Noel Joseph Raj,Vijayalakshmi G. V. Mahesh,Nersisson Ruban
  • Publisher : IGI Global
  • Release : 01 November 2020
GET THIS BOOK Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments

Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to