Deep Learning Models for Medical Imaging Book

Deep Learning Models for Medical Imaging


  • Author : K.C. Santosh
  • Publisher : Academic Press
  • Release Date : 2021-09-17
  • Genre: Computers
  • Pages : 170
  • ISBN 10 : 9780128236505
  • Total Read : 63
  • File Size : 5,9 Mb

DOWNLOAD BOOK
Deep Learning Models for Medical Imaging Summary:

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow ‘with’ and ‘without’ transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists. Provides a step-by-step approach to develop deep learning models Presents case studies showing end-to-end implementation (source codes: available upon request)

Machine Learning and Medical Imaging Book

Machine Learning and Medical Imaging


  • Author : Guorong Wu
  • Publisher : Academic Press
  • Release Date : 2016-08-11
  • Genre: Technology & Engineering
  • Pages : 512
  • ISBN 10 : 9780128041147
  • Total Read : 81
  • File Size : 18,6 Mb

DOWNLOAD BOOK
Machine Learning and Medical Imaging Summary:

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 Applications in Medical Imaging Book

Deep Learning Applications in Medical Imaging


  • Author : Saxena, Sanjay
  • Publisher : IGI Global
  • Release Date : 2020-10-16
  • Genre: Medical
  • Pages : 274
  • ISBN 10 : 9781799850724
  • Total Read : 67
  • File Size : 6,7 Mb

DOWNLOAD BOOK
Deep Learning Applications in Medical Imaging Summary:

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.

Deep Learning in Medical Image Analysis Book

Deep Learning in Medical Image Analysis


  • Author : Gobert Lee
  • Publisher : Springer Nature
  • Release Date : 2020-02-06
  • Genre: Medical
  • Pages : 181
  • ISBN 10 : 9783030331283
  • Total Read : 82
  • File Size : 17,7 Mb

DOWNLOAD BOOK
Deep Learning in Medical Image Analysis Summary:

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 image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Deep Learning for Medical Image Analysis Book

Deep Learning for Medical Image Analysis


  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Release Date : 2017-01-18
  • Genre: Computers
  • Pages : 458
  • ISBN 10 : 9780128104095
  • Total Read : 67
  • File Size : 7,8 Mb

DOWNLOAD BOOK
Deep Learning for Medical Image Analysis Summary:

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

Advances in Deep Learning for Medical Image Analysis Book

Advances in Deep Learning for Medical Image Analysis


  • Author : Archana Mire
  • Publisher : CRC Press
  • Release Date : 2022-04-28
  • Genre: Technology & Engineering
  • Pages : 168
  • ISBN 10 : 9781000575958
  • Total Read : 77
  • File Size : 6,5 Mb

DOWNLOAD BOOK
Advances in Deep Learning for Medical Image Analysis Summary:

This reference text introduces the classical probabilistic model, deep learning, and big data techniques for improving medical imaging and detecting various diseases. The text addresses a wide variety of application areas in medical imaging where deep learning techniques provide solutions with lesser human intervention and reduced time. It comprehensively covers important machine learning for signal analysis, deep learning techniques for cancer detection, diabetic cases, skin image analysis, Alzheimer’s disease detection, coronary disease detection, medical image forensic, fetal anomaly detection, and plant phytology. The text will serve as a useful text for graduate students and academic researchers in the fields of electronics engineering, computer science, biomedical engineering, and electrical engineering.

Medical Imaging Book
Score: 5
From 1 Ratings

Medical Imaging


  • Author : K.C. Santosh
  • Publisher : CRC Press
  • Release Date : 2019-08-20
  • Genre: Computers
  • Pages : 200
  • ISBN 10 : 9780429639326
  • Total Read : 95
  • File Size : 12,8 Mb

DOWNLOAD BOOK
Medical Imaging Summary:

The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

Approaches and Applications of Deep Learning in Virtual Medical Care Book

Approaches and Applications of Deep Learning in Virtual Medical Care


  • Author : Noor Zaman
  • Publisher : Medical Information Science Reference
  • Release Date : 2022
  • Genre: Uncategoriezed
  • Pages : 300
  • ISBN 10 : 1799889297
  • Total Read : 71
  • File Size : 12,6 Mb

DOWNLOAD BOOK
Approaches and Applications of Deep Learning in Virtual Medical Care Summary:

"The book will focus on Innovative approaches for medical sensor/image data analysis, event detection, segmentation, and abnormality detection, object/lesion classification, organ/region/landmark localization, object/lesion detection, organ/substructure segmentation, lesion segmentation, and medical image registration using deep learning"--