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Deep Learning Models For Medical Imaging

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Deep Learning Models for Medical Imaging

Deep Learning Models for Medical Imaging Book
Author : K.C. Santosh,Nibaran Das,Swarnendu Ghosh
Publisher : Academic Press
Release : 2021-09-17
ISBN : 0128236507
Language : En, Es, Fr & De

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Book Description :

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)

Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Understanding and Interpreting Machine Learning in Medical Image Computing Applications Book
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 : 2018-10-23
ISBN : 3030026280
Language : En, Es, Fr & De

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Book Description :

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 were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.

Deep Learning in Medical Image Analysis

Deep Learning in Medical Image Analysis Book
Author : Gobert Lee,Hiroshi Fujita
Publisher : Springer Nature
Release : 2020-02-06
ISBN : 3030331288
Language : En, Es, Fr & De

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Book Description :

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.

Enhancing Medical Imaging Workflows with Deep Learning

Enhancing Medical Imaging Workflows with Deep Learning Book
Author : Ken Chang (Ph. D.)
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 are collecting ever-increasing quantities of medical imaging. Together, deep learning models and big data yield a powerful combination. Integrated in the data workflow, the clinic, or at the bedside, these models have the potential to aid with clinical decision-making, improving efficiency, accuracy, and reliability of patient care. However, at present, there is a critical gap between the researchers who develop deep learning algorithms and the clinicians who could utilize the technology to improve patient care. In this thesis, I focus on several challenges that prevent clinical translation of algorithms. First, vast quantities of data needed to train effective models are often dispersed across institutions and cannot be shared due to ethical, infrastructure, and patient privacy concerns. As such, we developed distributed methods of training robust deep learning models that do not require sharing patient data in multi-institutional collaborative settings. Second, it is not clearly understood how decisions in algorithm design can affect model performance. To this end, I showcase how various training, data, and model parameters can impact algorithm prediction and performance. Lastly, while many algorithms are designed to perform a single task, there are few pipelines that have multi-faceted functionality needed in patient care. I demonstrate an integrated and deployable clinical decision support pipeline for glioma and ischemic stroke that is extensible to other diseases.

Medical Image Computing and Computer Assisted Intervention MICCAI 2021

Medical Image Computing and Computer Assisted Intervention     MICCAI 2021 Book
Author : Marleen de Bruijne,Philippe C. Cattin,Stéphane Cotin,Nicolas Padoy,Stefanie Speidel,Yefeng Zheng,Caroline Essert
Publisher : Springer Nature
Release : 2021-09-23
ISBN : 3030871967
Language : En, Es, Fr & De

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Book Description :

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: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertainty Part IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual reality Part V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease prediction Part VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascular Part VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncology Part VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound *The conference was held virtually.

Medical Image Computing and Computer Assisted Intervention MICCAI 2015

Medical Image Computing and Computer Assisted Intervention     MICCAI 2015 Book
Author : Nassir Navab,Joachim Hornegger,William M. Wells,Alejandro Frangi
Publisher : Springer
Release : 2015-09-28
ISBN : 3319245740
Language : En, Es, Fr & De

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Book Description :

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; advanced MRI: diffusion, fMRI, DCE; quantitative image analysis III: motion, deformation, development and degeneration; quantitative image analysis IV: microscopy, fluorescence and histological imagery; registration: method and advanced applications; reconstruction, image formation, advanced acquisition - computational imaging; modelling and simulation for diagnosis and interventional planning; computer-assisted and image-guided interventions.

Machine Learning in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio Signal Analysis and Diagnostic Imaging Book
Author : Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
Publisher : Academic Press
Release : 2018-11-30
ISBN : 012816087X
Language : En, Es, Fr & De

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Book Description :

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 PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging Book
Author : Heung-Il Suk,Mingxia Liu,Pingkun Yan,Chunfeng Lian
Publisher : Springer Nature
Release : 2019-10-09
ISBN : 3030326926
Language : En, Es, Fr & De

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Book Description :

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, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.

Simulation and Synthesis in Medical Imaging

Simulation and Synthesis in Medical Imaging Book
Author : Ninon Burgos,Ali Gooya,David Svoboda
Publisher : Springer Nature
Release : 2019-10-10
ISBN : 3030327787
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 4th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 16 full papers presented were carefully reviewed and selected from 21 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/PET/microscopy image synthesis, image super resolution, and several applications of image synthesis and simulation for data augmentation, segmentation or lesion detection.

Simulation and Synthesis in Medical Imaging

Simulation and Synthesis in Medical Imaging Book
Author : David Svoboda,Ninon Burgos,Jelmer M. Wolterink,Can Zhao
Publisher : Springer Nature
Release : 2021-09-21
ISBN : 303087592X
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 14 full papers presented were carefully reviewed and selected from 18 submissions. The contributions span the following broad categories in alignment with the initial call-for-papers: methods based on generative models or adversarial learning for MRI/CT/ microscopy image synthesis, and several applications of image synthesis and simulation for data augmentation, image enhancement, or segmentation. *The workshop was held virtually.

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Deep Learning and Convolutional Neural Networks for Medical Image Computing Book
Author : Le Lu,Yefeng Zheng,Gustavo Carneiro,Lin Yang
Publisher : Springer
Release : 2017-07-12
ISBN : 331942999X
Language : En, Es, Fr & De

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Book Description :

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 experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

Computational Analysis and Deep Learning for Medical Care

Computational Analysis and Deep Learning for Medical Care Book
Author : Amit Kumar Tyagi
Publisher : John Wiley & Sons
Release : 2021-08-10
ISBN : 1119785731
Language : En, Es, Fr & De

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Book Description :

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 belter results are highly effective for e-healthcare applications, which is the challenge of today. For that, this book provides some reliable deep leaning or deep neural networks models for healthcare applications via receiving chapters from around the world. In summary, this book will cover introduction, requirement, importance, issues and challenges, etc., faced in available current deep learning models (also include innovative deep learning algorithms/ models for curing disease in Medicare) and provide opportunities for several research communities with including several research gaps in deep learning models (for healthcare applications).

Pneumothorax Segmentation of Chest X rays Using a Stacked Generalization Framework with Multiple Convolutional Neural Networks

Pneumothorax Segmentation of Chest X rays Using a Stacked Generalization Framework with Multiple Convolutional Neural Networks Book
Author : Amol Mavuduru
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

With advances in deep learning research, convolutional neural networks (CNNs) have achieved state-of-the-art results in common computer vision tasks such as image classification and image segmentation. In the past decade, most of the research in the domain of CNNs has focused on optimizing the mathematical structure or architecture of CNNs to improve performance on these tasks. Nearly a decade after the introduction of the first CNN architecture, state-of-the-art CNNs have surpassed human performance on image classification and segmentation tasks. Given their capability to achieve super-human performance on image-recognition tasks, in recent research, CNNs have been trained to perform difficult medical imaging tasks such as cancer segmentation. While researchers studying image segmentation have focused on optimizing specific CNN architectures to perform well on complex tasks such as medical image segmentation, there is a significant amount of unexplored potential in applying ensemble machine learning techniques to combine the predictions of multiple CNN architectures to produce more robust models. Ensemble machine learning is an area of machine learning that involves combining the predictions of several machine learning models with techniques such as majority voting, averaging, and stacked generalization in order to produce models with lower generalization errors. Stacked generalization is a powerful technique that involves training a higher-level model that aggregates the predictions of lower models and each input instance to generate final predictions. This research proposes and evaluates a framework for applying stacked generalization to combine the predictions of multiple CNNs and improve performance on medical image segmentation tasks. The proposed method allows researchers to combine different state-of-theart CNN architectures into a larger neural network that uses the predictions of each individual CNN and the properties of the input image to generate a more accurate set of predictions. We evaluate the effectiveness of this method by comparing the performance of individual CNNs and the proposed method on a dataset for medical image segmentation from the 2019 SIIM ACR Kaggle Pneumothorax Segmentation Challenge.

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis Book
Author : S. Kevin Zhou,Hayit Greenspan,Dinggang Shen
Publisher : Academic Press
Release : 2017-01-18
ISBN : 0128104090
Language : En, Es, Fr & De

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Book Description :

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

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Perinatal Imaging Placental and Preterm Image Analysis

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging  and Perinatal Imaging  Placental and Preterm Image Analysis Book
Author : Carole H. Sudre,Roxane Licandro,Christian Baumgartner,Andrew Melbourne,Adrian Dalca,Jana Hutter,Ryutaro Tanno,Esra Abaci Turk,Koen Van Leemput,Jordina Torrents Barrena,William M. Wells,Christopher Macgowan
Publisher : Springer Nature
Release : 2021-09-30
ISBN : 3030877353
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the Third Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 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. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

Machine Learning Big Data and IoT for Medical Informatics

Machine Learning  Big Data  and IoT for Medical Informatics Book
Author : Pardeep Kumar,Yugal Kumar,Mohamed A. Tawhid
Publisher : Academic Press
Release : 2021-06-13
ISBN : 0128217812
Language : En, Es, Fr & De

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Book Description :

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT. Explains the uses of CNN, Deep Learning and extreme machine learning concepts for the design and development of predictive diagnostic systems. Includes several privacy preservation techniques for medical data. Presents the integration of Internet of Things with predictive diagnostic systems for disease diagnosis. Offers case studies and applications relating to machine learning, big data, and health care analysis.

Machine Learning in Medical Imaging

Machine Learning in Medical Imaging Book
Author : Yinghuan Shi,Heung-Il Suk,Mingxia Liu
Publisher : Springer
Release : 2018-09-14
ISBN : 303000919X
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the 9th International Workshop on Machine Learning in Medical Imaging, MLMI 2018, held in conjunction with MICCAI 2018 in Granada, Spain, in September 2018. The 45 papers presented in this volume were carefully reviewed and selected from 82 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 medical imaging.

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 Book
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 : 2019-10-10
ISBN : 3030326896
Language : En, Es, Fr & De

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Book Description :

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 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

Machine Learning and Medical Imaging

Machine Learning and Medical Imaging Book
Author : Guorong Wu,Dinggang Shen,Mert Sabuncu
Publisher : Academic Press
Release : 2016-08-11
ISBN : 0128041145
Language : En, Es, Fr & De

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Book Description :

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

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics Book
Author : R. Sujatha,S. L. Aarthy,R. Vettriselvan
Publisher : CRC Press
Release : 2021-09-22
ISBN : 1000454533
Language : En, Es, Fr & De

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Book Description :

Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.