Deep Learning Models for Medical Imaging

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  • Publisher : Academic Press
  • Release : 17 September 2021
  • ISBN : 9780128236505
  • Page : 170 pages
  • Rating : 4.5/5 from 103 voters

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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)

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Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging
  • Author : K.C. Santosh,Nibaran Das,Swarnendu Ghosh
  • Publisher : Academic Press
  • Release Date : 2021-09-17
  • ISBN : 9780128236505
GET THIS BOOKDeep Learning Models for Medical Imaging

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

Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging
  • Author : K.C. Santosh,Nibaran Das,Swarnendu Ghosh
  • Publisher : Elsevier
  • Release Date : 2021-10-15
  • ISBN : 9780128235041
GET THIS BOOKDeep Learning Models for Medical Imaging

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

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis
  • Author : Gobert Lee,Hiroshi Fujita
  • Publisher : Springer Nature
  • Release Date : 2020-02-06
  • ISBN : 9783030331283
GET THIS BOOKDeep 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 : Kenji Suzuki,Fei Wang,Dinggang Shen,Pingkun Yan
  • Publisher : Springer
  • Release Date : 2011-09-25
  • ISBN : 9783642243196
GET THIS BOOKMachine Learning in Medical Imaging

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning in Medical Imaging, MLMI 2011, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 44 revised full papers presented were carefully reviewed and selected from 74 submissions. The papers focus on major trends in machine learning in medical imaging aiming to identify new cutting-edge techniques and their use in medical imaging.

Deep Learning Applications in Medical Imaging

Deep Learning Applications in Medical Imaging
  • Author : Saxena, Sanjay,Paul, Sudip
  • Publisher : IGI Global
  • Release Date : 2020-10-16
  • ISBN : 9781799850724
GET THIS BOOKDeep Learning Applications in Medical Imaging

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

Advances in Deep Learning for Medical Image Analysis

Advances in Deep Learning for Medical Image Analysis
  • Author : Archana Mire,Vinayak Elangovan,Shailaja Patil
  • Publisher : CRC Press
  • Release Date : 2022-04-28
  • ISBN : 9781000575958
GET THIS BOOKAdvances in Deep Learning for Medical Image Analysis

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,

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
  • Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
  • Publisher : Academic Press
  • Release Date : 2017-01-18
  • ISBN : 9780128104095
GET THIS BOOKDeep 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

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 Date : 2017-07-12
  • ISBN : 9783319429991
GET THIS BOOKDeep 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

Medical Imaging

Medical Imaging
  • Author : K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
  • Publisher : CRC Press
  • Release Date : 2019-08-20
  • ISBN : 9780429642494
GET THIS BOOKMedical Imaging

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

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures
  • Author : Hayit Greenspan,Ryutaro Tanno,Marius Erdt,Tal Arbel,Christian Baumgartner,Adrian Dalca,Carole H. Sudre,William M. Wells,Klaus Drechsler,Marius George Linguraru,Cristina Oyarzun Laura,Raj Shekhar,Stefan Wesarg,Miguel Ángel González Ballester
  • Publisher : Springer Nature
  • Release Date : 2019-10-10
  • ISBN : 9783030326890
GET THIS BOOKUncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging
  • Author : Chunfeng Lian,Xiaohuan Cao,Islem Rekik,Xuanang Xu,Pingkun Yan
  • Publisher : Springer Nature
  • Release Date : 2021-09-25
  • ISBN : 9783030875893
GET THIS BOOKMachine Learning in Medical Imaging

This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned 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

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 Date : 2020-06-26
  • ISBN : 9781799835929
GET THIS BOOKDeep 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

Approaches and Applications of Deep Learning in Virtual Medical Care

Approaches and Applications of Deep Learning in Virtual Medical Care
  • Author : Noor Zaman,Loveleen Gaur,Mamoona Humayun
  • Publisher : Medical Information Science Reference
  • Release Date : 2022
  • ISBN : 1799889297
GET THIS BOOKApproaches and Applications of Deep Learning in Virtual Medical Care

"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"--

Artificial Intelligence in Medical Imaging

Artificial Intelligence in Medical Imaging
  • Author : Lia Morra,Silvia Delsanto,Loredana Correale
  • Publisher : CRC Press
  • Release Date : 2019-11-25
  • ISBN : 9781000753080
GET THIS BOOKArtificial Intelligence in Medical Imaging

This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed

Deep Learning for Chest Radiographs

Deep Learning for Chest Radiographs
  • Author : Yashvi Chandola,Jitendra Virmani,H.S Bhadauria,Papendra Kumar
  • Publisher : Elsevier
  • Release Date : 2021-07-16
  • ISBN : 9780323906869
GET THIS BOOKDeep Learning for Chest Radiographs

Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent