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 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

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  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Pages : 458 pages
  • ISBN : 0128104090
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKDeep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis
  • Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
  • Publisher : Academic Press
  • Release : 18 January 2017
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

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 : 28 April 2022
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 in Medical Image Analysis

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

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 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

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
  • Author : Danail Stoyanov,Zeike Taylor,Gustavo Carneiro,Tanveer Syeda-Mahmood,Anne Martel,Lena Maier-Hein,João Manuel R.S. Tavares,Andrew Bradley,João Paulo Papa,Vasileios Belagiannis,Jacinto C. Nascimento,Zhi Lu,Sailesh Conjeti,Mehdi Moradi,Hayit Greenspan,Anant Madabhushi
  • Publisher : Springer
  • Release : 19 September 2018
GET THIS BOOKDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The

Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging
  • Author : K.C. Santosh,Nibaran Das,Swarnendu Ghosh
  • Publisher : Academic Press
  • Release : 17 September 2021
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 and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
  • Author : M. Jorge Cardoso,Tal Arbel,Gustavo Carneiro,Tanveer Syeda-Mahmood,João Manuel R.S. Tavares,Mehdi Moradi,Andrew Bradley,Hayit Greenspan,João Paulo Papa,Anant Madabhushi,Jacinto C. Nascimento,Jaime S. Cardoso,Vasileios Belagiannis,Zhi Lu
  • Publisher : Springer
  • Release : 07 September 2017
GET THIS BOOKDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging
  • Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
  • Publisher : Academic Press
  • Release : 11 August 2016
GET THIS BOOKMachine Learning and Medical Imaging

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

Biomedical Data Mining for Information Retrieval

Biomedical Data Mining for Information Retrieval
  • Author : Subhendu Kumar Pani,Sujata Dash,S. Balamurugan,Ajith Abraham
  • Publisher : John Wiley & Sons
  • Release : 06 August 2021
GET THIS BOOKBiomedical Data Mining for Information Retrieval

This book comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including

Medical Image Recognition, Segmentation and Parsing

Medical Image Recognition, Segmentation and Parsing
  • Author : S. Kevin Zhou
  • Publisher : Academic Press
  • Release : 11 December 2015
GET THIS BOOKMedical Image Recognition, Segmentation and Parsing

This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods,

Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing
  • Author : Rohit Raja,Sandeep Kumar,Shilpa Rani,K. Ramya Laxmi
  • Publisher : CRC Press
  • Release : 23 December 2020
GET THIS BOOKArtificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and

Hybrid Image Processing Methods for Medical Image Examination

Hybrid Image Processing Methods for Medical Image Examination
  • Author : Venkatesan Rajinikanth,E Priya,Hong Lin,Fuhua Lin
  • Publisher : CRC Press
  • Release : 30 January 2021
GET THIS BOOKHybrid Image Processing Methods for Medical Image Examination

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation

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 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

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis
  • Author : Yu-Dong Dong Zhang,J M Górriz,Zhengchao Dong
  • Publisher : Mdpi AG
  • Release : 26 August 2021
GET THIS BOOKDeep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
  • Author : Nilanjan Dey
  • Publisher : Academic Press
  • Release : 31 July 2019
GET THIS BOOKClassification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep