Deep Learning Based Microscopy

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Deep Learning for Medical Image Analysis

Author : S. Kevin Zhou
Publisher : Academic Press
ISBN 13 : 0323858880
Total Pages : 544 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Deep Learning for Medical Image Analysis by : S. Kevin Zhou

Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-11-23 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is 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 are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest 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 cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Deep Learning for the Life Sciences

Author : Bharath Ramsundar
Publisher : O'Reilly Media
ISBN 13 : 1492039802
Total Pages : 236 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for the Life Sciences by : Bharath Ramsundar

Download or read book Deep Learning for the Life Sciences written by Bharath Ramsundar and published by O'Reilly Media. This book was released on 2019-04-10 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working

Deep Learning and Convolutional Neural Networks for Medical Image Computing

Author : Le Lu
Publisher : Springer
ISBN 13 : 331942999X
Total Pages : 327 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Deep Learning and Convolutional Neural Networks for Medical Image Computing by : Le Lu

Download or read book Deep Learning and Convolutional Neural Networks for Medical Image Computing written by Le Lu and published by Springer. This book was released on 2017-07-12 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning in Medical Image Analysis

Author : Gobert Lee
Publisher : Springer Nature
ISBN 13 : 3030331288
Total Pages : 184 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Deep Learning in Medical Image Analysis by : Gobert Lee

Download or read book Deep Learning in Medical Image Analysis written by Gobert Lee and published by Springer Nature. This book was released on 2020-02-06 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning and Medical Imaging

Author : Guorong Wu
Publisher : Academic Press
ISBN 13 : 0128041145
Total Pages : 514 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Machine Learning and Medical Imaging by : Guorong Wu

Download or read book Machine Learning and Medical Imaging written by Guorong Wu and published by Academic Press. This book was released on 2016-08-11 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Computer Vision for Microscopy Image Analysis

Author : Mei Chen
Publisher : Academic Press
ISBN 13 : 0128149736
Total Pages : 230 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Computer Vision for Microscopy Image Analysis by : Mei Chen

Download or read book Computer Vision for Microscopy Image Analysis written by Mei Chen and published by Academic Press. This book was released on 2020-12-01 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a computer scientist working on image analysis? Are you a biologist seeking tools to process the microscopy data from image-based experiments? Computer Vision for Microscopy Image Analysis provides a comprehensive and in-depth discussion of modern computer vision techniques, in particular deep learning, for microscopy image analysis that will advance your efforts. Progress in imaging techniques has enabled the acquisition of large volumes of microscopy data and made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "big visual data" into interpretable information. Visual analysis of large-scale microscopy data is a daunting task. Computer vision has the potential to automate this task. One key advantage is that computers perform analysis more reproducibly and less subjectively than human annotators. Moreover, high-throughput microscopy calls for effective and efficient techniques as there are not enough human resources to advance science by manual annotation. This book articulates the strong need for biologists and computer vision experts to collaborate to overcome the limits of human visual perception, and devotes a chapter each to the major steps in analyzing microscopy images, such as detection and segmentation, classification, tracking, and event detection. Discover how computer vision can automate and enhance the human assessment of microscopy images for discovery Grasp the state-of-the-art approaches, especially deep neural networks Learn where to obtain open-source datasets and software to jumpstart his or her own investigation

LED-Based Photoacoustic Imaging

Author : Mithun Kuniyil Ajith Singh
Publisher : Springer Nature
ISBN 13 : 9811539847
Total Pages : 393 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis LED-Based Photoacoustic Imaging by : Mithun Kuniyil Ajith Singh

Download or read book LED-Based Photoacoustic Imaging written by Mithun Kuniyil Ajith Singh and published by Springer Nature. This book was released on 2020-04-07 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the use of LEDs in biomedical photoacoustic imaging. In chapters written by key opinion leaders in the field, it covers a broad range of topics, including fundamentals, principles, instrumentation, image reconstruction and data/image processing methods, preclinical and clinical applications of LED-based photoacoustic imaging. Apart from preclinical imaging studies and early clinical pilot studies using LED-based photoacoustics, the book includes a chapter exploring the opportunities and challenges of clinical translation from an industry perspective. Given its scope, the book will appeal to scientists and engineers in academia and industry, as well as medical experts interested in the clinical applications of photoacoustic imaging.

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781665429474
Total Pages : pages
Book Rating : 4.4/5 (294 download)

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Book Synopsis 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) by : IEEE Staff

Download or read book 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) written by IEEE Staff and published by . This book was released on 2021-04-13 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging ISBI 2021 will be the 18th meeting in this series The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging The 2021 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation

Deep Learning Techniques for Music Generation

Author : Jean-Pierre Briot
Publisher : Springer
ISBN 13 : 3319701630
Total Pages : 303 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Deep Learning Techniques for Music Generation by : Jean-Pierre Briot

Download or read book Deep Learning Techniques for Music Generation written by Jean-Pierre Briot and published by Springer. This book was released on 2019-11-08 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.

Biomedical Data Mining for Information Retrieval

Author : Sujata Dash
Publisher : John Wiley & Sons
ISBN 13 : 111971124X
Total Pages : 450 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Biomedical Data Mining for Information Retrieval by : Sujata Dash

Download or read book Biomedical Data Mining for Information Retrieval written by Sujata Dash and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval 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 healthcare 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 patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Hyperspectral Image Analysis

Author : Saurabh Prasad
Publisher : Springer Nature
ISBN 13 : 3030386171
Total Pages : 464 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Hyperspectral Image Analysis by : Saurabh Prasad

Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.

Multiscale Multimodal Medical Imaging

Author : Quanzheng Li
Publisher : Springer Nature
ISBN 13 : 3030379698
Total Pages : 119 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Multiscale Multimodal Medical Imaging by : Quanzheng Li

Download or read book Multiscale Multimodal Medical Imaging written by Quanzheng Li and published by Springer Nature. This book was released on 2019-12-19 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 papers presented were carefully reviewed and selected from 18 submissions. The MMMI workshop aims to advance the state of the art in multi-scale multi-modal medical imaging, including algorithm development, implementation of methodology, and experimental studies. The papers focus on medical image analysis and machine learning, especially on machine learning methods for data fusion and multi-score learning.

Deep Learning and Data Labeling for Medical Applications

Author : Gustavo Carneiro
Publisher : Springer
ISBN 13 : 3319469762
Total Pages : 289 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Deep Learning and Data Labeling for Medical Applications by : Gustavo Carneiro

Download or read book Deep Learning and Data Labeling for Medical Applications written by Gustavo Carneiro and published by Springer. This book was released on 2016-10-07 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

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

Author : Danail Stoyanov
Publisher : Springer
ISBN 13 : 3030008894
Total Pages : 401 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support by : Danail Stoyanov

Download or read book Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support written by Danail Stoyanov and published by Springer. This book was released on 2018-09-19 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015

Author : Nassir Navab
Publisher : Springer
ISBN 13 : 3319245740
Total Pages : 801 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 by : Nassir Navab

Download or read book Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 written by Nassir Navab and published by Springer. This book was released on 2015-09-28 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Artificial Intelligence in Medical Imaging

Author : Erik R. Ranschaert
Publisher : Springer
ISBN 13 : 3319948784
Total Pages : 369 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Artificial Intelligence in Medical Imaging by : Erik R. Ranschaert

Download or read book Artificial Intelligence in Medical Imaging written by Erik R. Ranschaert and published by Springer. This book was released on 2019-01-29 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Machine Learning in Medical Imaging

Author : Mingxia Liu
Publisher : Springer Nature
ISBN 13 : 3030598616
Total Pages : 702 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Machine Learning in Medical Imaging by : Mingxia Liu

Download or read book Machine Learning in Medical Imaging written by Mingxia Liu and published by Springer Nature. This book was released on 2020-10-02 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 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 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.