Deep Learning Models for Medical Imaging

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  • Author : K.C. Santosh
  • Publisher : Academic Press
  • Pages : 170 pages
  • ISBN : 0128236507
  • 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 17 September 2021 with total page 170 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 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)

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 BOOK Deep 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 Methods for Medical Image Computing

Deep Learning Methods for Medical Image Computing
  • Author : Haofu Liao
  • Publisher : Unknown
  • Release : 18 January 2022
GET THIS BOOK Deep Learning Methods for Medical Image Computing

"A long-standing goal of the medical community is to present and analyze medical images efficiently and intelligently. On the one hand, it means to find efficient ways to acquire high-quality medical images that can readily be used by healthcare providers. On the other hand, it means to discover intelligent ways to interpret medical images to facilitate the healthcare delivery. To this end, researchers and medical professionals usually seek to use computerized systems that are empowered by machine learning techniques for

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

Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Understanding and Interpreting Machine Learning in Medical Image Computing Applications
  • Author : Danail Stoyanov,Zeike Taylor,Seyed Mostafa Kia,Ipek Oguz,Mauricio Reyes,Anne Martel,Lena Maier-Hein,Andre F. Marquand,Edouard Duchesnay,Tommy Löfstedt,Bennett Landman,M. Jorge Cardoso,Carlos A. Silva,Sergio Pereira,Raphael Meier
  • Publisher : Springer
  • Release : 23 October 2018
GET THIS BOOK Understanding and Interpreting Machine Learning in Medical Image Computing Applications

This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 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 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume

Enhancing Medical Imaging Workflows with Deep Learning

Enhancing Medical Imaging Workflows with Deep Learning
  • Author : Ken Chang (Ph. D.)
  • Publisher : Unknown
  • Release : 18 January 2022
GET THIS BOOK Enhancing Medical Imaging Workflows with Deep Learning

The last few years mark a significant leap in the capability of algorithms with the advent of deep learning. While conventional machine learning has existed for decades, their utility has been rather limited, requiring considerable engineering and domain expertise to design pertinent data features that can be extracted from raw data. In contrast, deep learning methods have yielded state-of-the-art results in a wide range of computer vision tasks without the need for hand-crafted imaging features. At the same time, we

Big Data in Multimodal Medical Imaging

Big Data in Multimodal Medical Imaging
  • Author : Ayman El-Baz,Jasjit S. Suri
  • Publisher : CRC Press
  • Release : 06 November 2019
GET THIS BOOK Big Data in Multimodal Medical Imaging

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of

Computational Analysis and Deep Learning for Medical Care

Computational Analysis and Deep Learning for Medical Care
  • Author : Amit Kumar Tyagi
  • Publisher : John Wiley & Sons
  • Release : 10 August 2021
GET THIS BOOK Computational Analysis and Deep Learning for Medical Care

This book discuss how deep learning can help healthcare images or text data in making useful decisions”. For that, the need of reliable deep learning models like Neural networks, Convolutional neural network, Backpropagation, Recurrent neural network is increasing in medical image processing, i.e., in Colorization of Black and white images of X-Ray, automatic machine translation, object classification in photographs / images (CT-SCAN), character or useful generation (ECG), image caption generation, etc. Hence, Reliable Deep Learning methods for perception or producing

Medical Imaging

Medical Imaging
  • Author : K.C. Santosh,Sameer Antani,DS Guru,Nilanjan Dey
  • Publisher : CRC Press
  • Release : 20 August 2019
GET THIS BOOK Medical 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

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques
  • Author : Jyotismita Chaki
  • Publisher : Academic Press
  • Release : 13 December 2021
GET THIS BOOK Brain Tumor MRI Image Segmentation Using Deep Learning Techniques

Brain Tumor MRI Image Segmentation Using Deep Learning Techniques offers a description of deep learning approaches used for the segmentation of brain tumors. The book demonstrates core concepts of deep learning algorithms by using diagrams, data tables and examples to illustrate brain tumor segmentation. After introducing basic concepts of deep learning-based brain tumor segmentation, sections cover techniques for modeling, segmentation and properties. A focus is placed on the application of different types of convolutional neural networks, like single path, multi

Medical Image Computing and Computer Assisted Intervention MICCAI 2021

Medical Image Computing and Computer Assisted Intervention     MICCAI 2021
  • Author : Marleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert
  • Publisher : Springer Nature
  • Release : 23 September 2021
GET THIS BOOK Medical Image Computing and Computer Assisted Intervention MICCAI 2021

The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.* The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: image segmentation Part II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learning Part III:

Medical Image Computing and Computer Assisted Intervention MICCAI 2015

Medical Image Computing and Computer Assisted Intervention     MICCAI 2015
  • Author : Nassir Navab,Joachim Hornegger,William M. Wells,Alejandro Frangi
  • Publisher : Springer
  • Release : 28 September 2015
GET THIS BOOK Medical Image Computing and Computer Assisted Intervention MICCAI 2015

The three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. Based on rigorous peer reviews, the program committee carefully selected 263 revised papers from 810 submissions for presentation in three volumes. The papers have been organized in the following topical sections: quantitative image analysis I: segmentation and measurement; computer-aided diagnosis: machine learning; computer-aided diagnosis: automation; quantitative image analysis II: classification, detection, features, and morphology;

Deep Representation Learning from Imbalanced Medical Imaging

Deep Representation Learning from Imbalanced Medical Imaging
  • Author : Mina Rezaei
  • Publisher : Unknown
  • Release : 18 January 2022
GET THIS BOOK Deep Representation Learning from Imbalanced Medical Imaging

Medical imaging plays an important role in disease diagnosis, treatment planning, and clinical monitoring. One of the major challenges in medical image analysis is imbalanced training data, in which the class of interest is much rarer than the other classes. Canonical machine learning algorithms suppose that the number of samples from different classes in the training dataset is roughly similar or balance. Training a machine learning model on an imbalanced dataset can introduce unique challenges to the learning problem. A

Medical Image Understanding and Analysis

Medical Image Understanding and Analysis
  • Author : Bartłomiej W. Papież,Mohammad Yaqub,Jianbo Jiao,Ana I. L. Namburete,J. Alison Noble
  • Publisher : Springer Nature
  • Release : 06 July 2021
GET THIS BOOK Medical Image Understanding and Analysis

This book constitutes the refereed proceedings of the 25th Conference on Medical Image Understanding and Analysis, MIUA 2021, held in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 32 full papers and 8 short papers presented were carefully reviewed and selected from 77 submissions. They were organized according to following topical sections: biomarker detection; image registration, and reconstruction; image segmentation; generative models, biomedical simulation and modelling; classification; image enhancement, quality assessment, and data privacy; radiomics, predictive models, and quantitative imaging.

Machine Learning in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio Signal Analysis and Diagnostic Imaging
  • Author : Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
  • Publisher : Academic Press
  • Release : 30 November 2018
GET THIS BOOK Machine Learning in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as