Author: Bharath Ramsundar Publisher: O'Reilly Media ISBN: 1492039802 Category : Science Languages : en Pages : 236
Book Description
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
Author: S. Kevin Zhou Publisher: Academic Press ISBN: 0323858880 Category : Computers Languages : en Pages : 544
Book Description
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
Author: Vahid Ebrahimi Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Fluorescence microscopy has been a valuable tool in the field of biological science as it allows one to study the structure and interaction of protein complexes and organelles in living cells. However, conventional optical microscopy technique has been limited by a trade-off between spatiotemporal resolution, signal contrast, and photodamage to the biological samples. It means that an increase in spatial resolution or signal contrast comes at the cost of higher laser power, serial-scanning, or longer image acquisition time. Unfortunately, this leads to severe photobleaching and photodamage to the samples and/or limited throughput of imaging, which is highly challenging to be circumvented through only optical imaging technique. Therefore, one has turned to artificial intelligence (AI) in image processing, applying deep learning algorithms to different imaging modalities to overcome these traditional limitations in optical microscopy systems. Herein we present multiple strategies on how deep learning can be applied to solve challenging and fundamental problems in different fluorescence microscopy modalities. To do so, we present UNet-RCAN, a two-step deep learning network architecture based on a residual U-Net and residual channel attention network (RCAN) for image restoration. We demonstrate that UNet-RCAN achieves higher prediction accuracy compared to other state-of-the-art deep learning algorithms while maintaining the resolution of an output image compared to ground-truth data acquired with optical microscopes. We applied our method to three fluorescence imaging modalities. Firstly, we successfully demonstrate that UNet-RCAN can achieve up to two orders of magnitude acceleration in stimulated emission depletion (STED) imaging while maintaining super-resolution. This significant acceleration enables mitigation of photobleaching and photodamage by robust restoration of noisy 2D and 3D STED images from multiple targets as well as live-cell STED imaging of inner-mitochondrial dynamics with a ten-fold increase in the number of acquired frames compared to conventional STED microscopy. Secondly, we apply our approach in restoring high-resolution widefield deconvolution images of living cells with low light intensity and low photodamage. We show that the accuracy of deconvolution can significantly improve after image restoration with deep learning. Lastly, we show the application of UNet-RCAN in the resolution enhancement of single-shot volumetric imaging with a low numerical aperture objective lens.
Author: Mei Chen Publisher: Academic Press ISBN: 0128149736 Category : Computers Languages : en Pages : 230
Book Description
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
Author: Le Lu Publisher: Springer ISBN: 331942999X Category : Computers Languages : en Pages : 327
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.
Author: Guorong Wu Publisher: Academic Press ISBN: 0128041145 Category : Computers Languages : en Pages : 514
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
Author: Raj, Alex Noel Joseph Publisher: IGI Global ISBN: 1799866920 Category : Computers Languages : en Pages : 381
Book Description
Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.
Author: S. Kevin Zhou Publisher: Academic Press ISBN: 0128104090 Category : Computers Languages : en Pages : 460
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
Author: Kevin De Haan Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Over the past decade, deep learning has become one of the leading techniques used in the field of image processing. Beyond popular tasks in computer vision such as classification and segmentation, it has proven to be revolutionary in its applications for image enhancement and transformations. It has significantly changed the field of computational optics - and neural networks can now be used to accurately and rapidly solve a wide variety of inverse problems in microscopy. This dissertation discusses a few major classes of inverse imaging problems that can be solved using deep learning. The dissertation first presents, a framework that can be used to enhance microscopy images using single image super-resolution . This framework has been proven to be effective at super-resolving images captured with a holographic microscope that are resolution limited both by the number of pixels used for imaging, as well as by the numerical aperture (NA) of the microscope. The effectiveness of this same general framework beyond optical microscopy, will be further demonstrated by super resolving electron microscopy images. Next, the dissertation will show that a similar super-resolution framework can be extended to perform a transformation between two imaging modalities and improve the overall quality of images by using it to enhance images of thin blood smears captured by a cost-effective mobile-phone microscope. By enhancing mobile phone microscopy images to match the quality of a top-of-the-line benchtop microscope, the images are standardized, have their resolution improved and have aberrations removed, allowing the images to be used for screening of sickle cell disease. Using a deep learning based classification framework, 98% accuracy was achieved during blind tested of 96 human blood smear slides. Furthermore, by enhancing the images, the image quality is brought to a level which can be used by clinicians for further analysis if required. Finally, this same framework will be used to transform microscopy images and generate images from that are equivalent to those which have undergone chemical labeling and show some of the many applications of this technology. The technique was applied to virtual staining of label-free thin histological tissue sections which were used to generate multiple stains from a single tissue section, enabling different stains to be performed at the microscopic level, as well as blending of stains together - creating entirely new digital stains. This dissertation shows how multiple virtual stains can be used to generate synthetic datasets of perfectly matched stains, allowing downstream networks be trained to perform transformations between stains. The efficacy of three of these stain transformation networks - generating the Masson's trichrome, Jones silver stain, and periodic acid-Schiff stains from hematoxylin and eosin-stained kidney tissue are demonstrated in a diagnostic study, with the results showing the improvement that such technology can bring to patient care.
Author: Bharath Ramsundar
Author: S. Kevin Zhou
Author: Tim Scherr
Author: Vahid Ebrahimi
Author: Mei Chen
Author: Le Lu
Author: Guorong Wu
Author: Raj, Alex Noel Joseph
Author: S. Kevin Zhou
Author: Kevin De Haan