Download or read book A Residual Recurrent Convolutional Neural Network for Image Superresolution with Whole Slide Images written by Jesse Lynch and published by . This book was released on 2019 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presented is a deep learning based computational approach to solve the problem of enhancing the resolution of images gained from commonly available low magnification scanners, also known as the image super-resolution (SR) problem. The given class of scanner produces microscopic images relatively fast and has the advantage of storage efficiency. However, those scanners generate comparatively low quality images compared to images from complex and sophisticated higher cost, lower availability scanners and do not have the necessary resolution for diagnostic or clinical research. Therefore, low resolutions scanners are not in demand for these purposes. The motivation of this research is to determine whether an image with low resolution could be enhanced by applying a deep learning framework, resulting in an image that could serve the same diagnostic purposes as a high resolution image from expensive scanners or microscopes. Here, proposed are various models built onto a Recurrent Convolutional Neural Network (RCNN), with primary emphasis placed on a Residual Recurrent Convolutional Neural Network (RRCNN). The RRCNN created is a supervised machine learning method to process images that has properties of convolutional, residual, and recurrent neural networks. These models are specifically trained to take a low-resolution microscopic image from one of two tissue micro-arrays (TMAs) and transform it into a high-resolution image. Validation of these resolution improvements with computational analysis is done to show quantitative results for reconstructed images. The experiments completed demonstrate that some of the models produce images which are of similar quality to images from high resolution scanners, opening up new possibilities for research or clinical use.

Download or read book Superresolution Recurrent Convolutional Neural Networks for Learning with Multi resolution Whole Slide Images written by Huu Dat Bui and published by . This book was released on 2018 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: A recurrent convolutional neural network is supervised machine learning way to process images that has both properties of convolutional and recurrent networks. We propose Convolutional Neural Network (CNN) based approach and its advanced recurrent version (RCNN) to solve the problem of enhancing the resolution of images obtained from a low magnification scanner, also known as the image super-resolution (SR) problem. The given class of scanner produces microscopic images relatively fast and storage efficiently. However, those scanners generate comparatively low quality images than images from complex and sophisticated scanners and do not have the necessary resolution for diagnostic or clinical researches, therefore low resolutions scanners are not in demand. The motivation of this study is to determine whether an image with low resolution could be enhanced by applying deep learning framework such that it would serve the same diagnostic purpose as a high resolution image from expensive scanners or microscopes. We presented novel network design and complex loss function. We validate these resolution improvements with computational analysis to show an enhanced image give the same quantitative results. In summary, our extensive experiments demonstrate that this method indeed produces images which are same quality to images from high resolution scanners. This approach opens up new application possibilities for using low-resolution scanners not only in terms of cost but also in access and speed of scanning for both research and possible clinical use.

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2020 written by Anne L. Martel and published by Springer Nature. This book was released on 2020-10-02 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Download or read book Biomedical Image Synthesis and Simulation written by Ninon Burgos and published by Academic Press. This book was released on 2022-06-18 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future. - Gives state-of-the-art methods in (bio)medical image synthesis - Explains the principles (background) of image synthesis methods - Presents the main applications of biomedical image synthesis methods

Download or read book Deep Learning Based Image Super Resolution written by Xiang Wang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Image super resolution is one of the most significant computer vision researches aiming to reconstruct high resolution images with realistic details from low resolution images. In the past years, a number of traditional methods intended to produce high resolution images. Recently, Deep Convolutional Neural Networks (DCNNs) have developed rapidly and achieved impressive progress in the computer vision area. Benefiting from DCNNs, the performance of image super resolution has improved compared with traditional methods. However, there still exists a large gap between the results of current methods and the real-world high resolution quality. In this thesis, we leverage the techniques of DCNNs to develop image super res- olution models for generating satisfactory high resolution images. There are several proposed methods in this thesis to satisfy different super resolution scenarios. Our proposed methods are based on Generative Adversarial Networks (GANs), leading to powerful generative ability and effective discriminative learning. To breakthrough current bottlenecks, we design novel architectures for generator and discriminator, and involve new optimization strategies to improve the learning stability of the mod- els. In order to improve the generalization ability of proposed methods, we conduct two mainstream super resolution tasks, namely face image hallucination and natu- ral image super resolution. All the proposed components of our methods result in promising super resolution performance for these tasks. Not only handling the supervised super resolution task, we also investigate the more challenging problem, namely the unsupervised image super resolution task where the paired high resolution image and low resolution image data are unavailable. To evaluate the performance of our methods in different scenarios, we conduct exten- sive experiments on several benchmark datasets to study each method separately. Compared to state-of-the-art methods, our methods are able to achieve superior per- formance both quantitatively and qualitatively.

Download or read book Machine Learning Hybridization and Optimization for Intelligent Applications written by Tanvir Habib Sardar and published by CRC Press. This book was released on 2024-10-28 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses state-of-the-art reviews of the existing machine learning techniques and algorithms including hybridizations and optimizations. It covers applications of machine learning via artificial intelligence (AI) prediction tools, discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation. Features: • Focuses on hybridization and optimization of machine learning techniques. • Reviews supervised, unsupervised, and reinforcement learning using case study-based applications. • Covers the latest machine learning applications in as diverse domains as the Internet of Things, data science, cloud computing, and distributed and parallel computing. • Explains computing models using real-world examples and dataset-based experiments. • Includes case study-based explanations and usage for machine learning technologies and applications. This book is aimed at graduate students and researchers in machine learning, artificial intelligence, and electrical engineering.

Download or read book Enhanced Image Super resolution Technique Using Convolutional Neural Network written by Keong Chua Kah and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improved Deep Convolutional Neural Networks DCNN Approaches for Computer Vision and Bio medical Imaging written by Md Zahangir Alom and published by . This book was released on 2018 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is showing tremendous success in variety of application domains and demonstrates state-of-the-art performance over traditional machine learning approaches in the fields of Computer Vision, Speech Recognition, Natural Language Processing (NLP), Bio-Medical imaging, Computational Pathology, and many more. This thesis presents several improved Deep Convolutional Neural Network (DCNN) models including the Inception Recurrent Convolutional Neural Network (IRCNN) and Inception Recurrent Residual Convolutional Neural Networks (IRRCNN), a Recurrent U-Net (RU-Net), a Recurrent Residual U-Net (R2U-Net) model, a R2U-Net regression model, and a Densely Connected Recurrent Network (DCRN). These models are evaluated for classification, segmentation, and detection tasks in computer vision, Bio-medical imaging, and computational pathology applications. There are four key contribution areas in this thesis.The first contribution area is the introduction of two improved DCNN models for classification tasks: IRCNN and IRRCNN, which utilize the power of the Recurrent Convolutional Neural Network (RCNN), the Inception Network, and the Residual Network (ResNet). In addition, we have evaluated the impact of recurrent convolutional layers on DenseNet which is called Densely Connected Recurrent Network (DCRN). The performance of the IRCNN, DCRN, and IRRCNN models was investigated with a set of experiments and computer vision tasks where we used several publicly available datasets including MNIST, CIFAR 10, CIFAR 100, SVHN, CU3D-100, and Tiny ImageNet-200. The experimental results show that IRCNN, DCRN, and IRRCNN provide superior performance compared to the equivalent DCNN based methods including equivalent RCNN, ResNet, Inception V3, DenseNet, and Inception Residual Network (Inception V-4) with the same number of network parameters for different computer vision tasks. The second contribution area is the introduction of two different models including a Recurrent U-Net and Recurrent Residual U-Net models, which are named RU-Net and R2U-Net respectively. The proposed models utilize the power of U-Net, the Residual Network, the RCNN, and U-Net for image segmentation tasks. These proposed architectures have several advantages for segmentation tasks over the existing DL methods. First, a residual unit helps when training deep architectures. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with the same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets for blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including SegNet, U-Net and Residual U-Net (ResU-Net) in different Bio-medical segmentation tasks.The third contribution area is the introduction of an R2U-Net based regression model which is named University of Dayton Network (UD-Net) and is used for end-to-end detection tasks in digital pathology. To generalize these advanced DCNN models, we have applied classification, segmentation, and detection tasks in Digital Pathology Image Analysis (DPIA) including: microscopic blood cell classification, Breast Cancer Classification (BCC), invasive ductal carcinoma detection, and lymphoma classification, nuclei segmentation, epithelium segmentation, tubule segmentation, lymphocyte detection, and mitosis detection. The experiments have been conducted on different publicly available datasets and evaluated with different performance metrics. The results demonstrate superior performance compared to existing DCNN based methods. The fourth contribution area is the introduction of an image reconstruction technique using Convolutional Sparse Coding (CSC) on IBM's TrueNorth Neuromorphic computing system and the results demonstrate promising sparse reconstructions for two different benchmarks: MNIST and CIFAR-10. In 2016, IBM's release of a deep learning framework for DCNNs called Energy Efficient Deep Neuromorphic Networks (EEDN). EEDN shows promise for delivering high accuracies across different benchmark while consuming very low power using IBM's TrueNorth chip. We have empirically evaluated the performance of different DCNN architectures implemented within the EEDN framework to discover the most efficient way to implement DCNN models for object classification tasks using the TrueNorth system. The results show that for datasets with large numbers of classes, wider networks perform better when compared to deep networks comprised of nearly the same core complexity on IBM's TrueNorth system. In addition, we have proposed an effective quantization approach for Recurrent Neural Networks (RNN): Long Short-Term Memory (SLTM), Gated Recurrent Unit (GRU), and Convolutional LSTM (ConvLSTM). Furthermore, an NP-hard optimization problem called Quadratic Unconstrained Binary Optimization (QUBO) has solved with vanilla RNN on IBM's Neuromorphic computing system.

Download or read book Computer Vision ACCV 2014 written by Daniel Cremers and published by Springer. This book was released on 2015-04-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set LNCS 9003--9007 constitutes the thoroughly refereed post-conference proceedings of the 12th Asian Conference on Computer Vision, ACCV 2014, held in Singapore, Singapore, in November 2014. The total of 227 contributions presented in these volumes was carefully reviewed and selected from 814 submissions. The papers are organized in topical sections on recognition; 3D vision; low-level vision and features; segmentation; face and gesture, tracking; stereo, physics, video and events; and poster sessions 1-3.

Download or read book Deep Learning Based Image Super resolution with Adaption and Extension of Convolutional Neural Network Models written by Ha Viet Khanh and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multimodal Brain Tumor Segmentation and Beyond written by Bjoern Menze and published by Frontiers Media SA. This book was released on 2021-08-10 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Polarimetric Doppler Weather Radar written by V. N. Bringi and published by Cambridge University Press. This book was released on 2001-08-30 with total page 666 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2001 book provides a detailed introduction to the principles of Doppler and polarimetric radar, focusing in particular on their use in the analysis of weather systems. The design features and operation of practical radar systems are highlighted throughout the book in order to illustrate important theoretical foundations. The authors begin by discussing background topics such as electromagnetic scattering, polarization, and wave propagation. They then deal in detail with the engineering aspects of pulsed Doppler polarimetric radar, including the relevant signal theory, spectral estimation techniques, and noise considerations. They close by examining a range of key applications in meteorology and remote sensing. The book will be of great use to graduate students of electrical engineering and atmospheric science as well as to practitioners involved in the applications of polarimetric radar systems.

Download or read book Guide to Convolutional Neural Networks written by Hamed Habibi Aghdam and published by Springer. This book was released on 2017-05-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Download or read book CNN based Single Image Super resolution Network and Biomedical Image Applications written by Samet Bayram and published by . This book was released on 2018 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we propose a convolutional neural network (CNN) based single image super-resolution network model with sparse representation by combining three existing state-of-the-art methods SC \cite{sr-sc}, SRCNN \cite{srcnn} and SCN\cite{scn} models with a modified pre-processing step. Firstly, standard gaussian box filter is applied to test image, which is a low-resolution image (LR), to remove low-frequency noises. After that, the given low-resolution image is up-scaled by bicubic interpolation method to the same size with desired output high-resolution image (HR). Secondly, a convolutional neural network based dictionary learning method is employed to train input low-resolution image to obtain LR image patches. Also, a rectified linear unit (ReLU) thresholds the output of the CNN to get a better LR image dictionary. Thirdly, to get optimal sparse parameters, we adopted Learned Iterative Shrinkage and Thresholding Algorithm (LISTA)\cite{lista15} \cite{lista16} network to train LR image patches. LISTA is a sparse-based network that generates sparse coefficients from each LR image patches. Finally, in the reconstruction step, corresponding high-resolution image patches are obtained by multiplying low-resolution image patches with optimal sparse coefficients. Then corresponding high-resolution image patches are combined to get final HR image. The experimental results show that our proposed method demonstrates outstanding performance compare to other state-of-the-art. The proposed method generates clear and better-detailed output high-resolution images since it is important in real life applications. The advantage of the proposed method is to combine convolutional neural network based dictionary learning and sparse-based network training with better pre-processing to create efficient and flexible single-image-super-resolution network.

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.

Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-05 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

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.