Download or read book Deep Learning Based Multimodal Retinal Image Processing written by Yiqian Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The retina, the light sensitive tissue lining the interior of the eye, is the only part of the central nervous system (CNS) that can be imaged at micron resolution in vivo. Retinal diseases including age-related macular degeneration, diabetes retinopathy, and vascular occlusions are important causes of vision loss and have systemic implications for millions of patients. Retinal imaging is of great significance to diagnosing and monitoring both retinal diseases and systematic diseases that manifest in the retina. A variety of imaging devices have been developed, including color fundus (CF) photography, infrared reflectance (IR), fundus autofluorescence (FAF), dye-based angiography, optical coherence tomography (OCT), and OCT angiography (OCT-A). Each imaging modality is particularly useful for observing certain aspects of the retina, and can be utilized for visualization of specific diseases. In this dissertation, we propose deep learning based methods for retinal image processing, including multimodal retinal image registration, OCT motion correction, and OCT retinal layer segmentation. We present our established work on a deep learning framework for multimodal retinal image registration, a comprehensive study of the correlation between subjective and objective evaluation metrics for multimodal retinal image registration, convolutional neural networks for correction of axial and coronal motion artifacts in 3D OCT volumes, and joint motion correction and 3D OCT layer segmentation network. The dissertation not only proposes novel approaches in image processing, enhances the observation of retinal diseases, but will also provide insights on observing systematic diseases through the retina, including diabetes, cardiovascular disease, and preclinical Alzheimer's Disease. The proposed deep learning based retinal image processing approaches would build a connection between ophthalmology and image processing literature, and the findings may provide a good insight for researchers who investigate retinal image registration, retinal image segmentation and retinal disease detection.

Download or read book Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support written by Kenji Suzuki and published by Springer Nature. This book was released on 2019-10-24 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.

Download or read book Deep Learning Based Image Processing written by Ying Liu and published by Eliva Press. This book was released on 2022-09-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning enables a model constituted by multiple processing layers to learn the data representation with multiple levels of abstraction. In the past decade, deep learning has brought remarkable achievements in many fields of machine learning and pattern recognition, especially in image processing. The state-of-the-art performance in image super-resolution reconstruction, image classification, target detection, image retrieval and other image processing tasks have been greatly improved. This book introduces these image processing technologies based on deep learning, including recent advances, applications in real scenes and future trends. The first chapter introduces image super-resolution reconstruction, which aims to recover high-resolution images from corresponding low-resolution versions. This chapter reviews these image super-resolution methods based on convolutional neural networks and generative adversarial networks on account of internal network structure. The second chapter presents four categories of few-shot image classification algorithms: transfer learning based, meta-learning based, data augmentation based, and multimodal based methods. In the third chapter, deep learning based models for small target detection in video are summarized in detail, which are categorized into one-stage models and two-stage models according to the detection stages. The network structures and plug-in modules for video based small target detection are also explained. The fourth chapter discusses deep learning based cross-modal hashing for image retrieval methods, including the extraction of high-level semantic information and the maintenance of similarity between different mo

Download or read book Artificial Intelligence in Ophthalmology written by Andrzej Grzybowski and published by Springer Nature. This book was released on 2021-10-13 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a wide-ranging overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms in ophthalmology. Expertly written chapters examine AI in age-related macular degeneration, glaucoma, retinopathy of prematurity and diabetic retinopathy screening. AI perspectives, systems and limitations are all carefully assessed throughout the book as well as the technical aspects of DL systems for retinal diseases including the application of Google DeepMind, the Singapore algorithm, and the Johns Hopkins algorithm. Artificial Intelligence in Ophthalmology meets the need for a resource that reviews the benefits and pitfalls of AI, ML and DL in ophthalmology. Ophthalmologists, optometrists, eye-care workers, neurologists, cardiologists, internal medicine specialists, AI engineers and IT specialists with an interest in how AI can help with early diagnosis and monitoring treatment in ophthalmic patients will find this book to be an indispensable guide to an evolving area of healthcare technology.

Download or read book Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments written by Raj, Alex Noel Joseph and published by IGI Global. This book was released on 2020-12-25 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Computational Retinal Image Analysis written by Emanuele Trucco and published by Academic Press. This book was released on 2019-11-19 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies

Download or read book Multi Modal Retinal Image Registration Via Deep Neural Networks written by Junkang Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-modal retinal images provide complementary anatomical information at various resolutions, color wavelengths, and fields of view. Aligning multi-modal images will establish a comprehensive view of the retina and benefit the screening and diagnosis of eye diseases. However, the inconsistent anatomical patterns across modalities create outliers in feature matching, and the lack of retinal boundaries may also fool the intensity-based alignment metrics, both of which will influence the alignment qualities. Besides, the varying distortion levels across Ultra-Widefield (UWF) and Narrow-Angle (NA) images, due to different camera parameters, will cause large alignment errors in global transformation. In addressing the issue of inconsistent patterns, we use retinal vasculature as a common signal for alignment. First, we build a two-step coarse-to-fine registration pipeline fully based on deep neural networks. The coarse alignment step estimates a global transformation via vessel segmentation, feature detection and description, and outlier rejection. While the fine alignment step corrects the remaining misalignment through deformable registration. In addition, we propose an unsupervised learning scheme based on style transfer to jointly train the networks for vessel segmentation and deformable registration. Finally, we also introduce Monogenical Phase signal as an alternative guidance in training the deformable registration network. Then, to deal with the issue of various distortion levels across UWF and NA modalities, we propose a distortion correction function to create images with similar distortion levels. Based on the assumptions of spherical eyeball shape and fixed UWF camera pose, the function reprojects the UWF pixels by an estimated correction camera with similar parameters as the NA camera. Besides, we incorporate the function into the coarse alignment networks which will simultaneously optimize the correction camera pose and refine the global alignment results. Moreover, to further reduce misalignment from the UWF-to-NA global registration, we estimate a 3D dense scene for the UWF pixels to represent a more flexible eyeball shape. Both the scene and the NA camera parameters are iteratively optimized to reduce the alignment error between the 3D-to-2D reprojected images and the original ones, which is also concatenated with the coarse alignment networks with distortion correction function.

Download or read book Computational Methods and Deep Learning for Ophthalmology written by D. Jude Hemanth and published by Elsevier. This book was released on 2023-02-18 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye. Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders. This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration. Presents the latest computational methods for designing and using Decision-Support Systems for ophthalmologic disorders in the human eye Conveys the role of a variety of computational methods and algorithms for efficient and effective diagnosis of ophthalmologic disorders, including Diabetic Retinopathy, Glaucoma, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders Explains how to develop and apply a variety of computational diagnosis systems and technologies, including medical image processing algorithms, bioinspired optimization, Deep Learning, computational intelligence systems, fuzzy-based segmentation methods, transfer learning approaches, and hybrid Artificial Neural Networks

Download or read book Multimodal Scene Understanding written by Michael Yang and published by Academic Press. This book was released on 2019-07-16 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Download or read book Retinal Image Analysis Based on Deep Learning written by B. Q. Al-Bander and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Computer aided diagnosis for Ocular Abnormalities from a Single Color Fundus Photography with Deep Learning written by Anneke Annassia Putri Siswadi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Any damage to the retina can lead to severe consequences like blindness. This visual impairment is preventable by early detection of ocular abnormalities. Computer-aided diagnosis (CAD) for ocular abnormalities is built by analyzing retinal imaging modalities, for instance, Color Fundus Photography (CFP). The main objectives of this thesis are to build two CAD models, one to detect the microaneurysms (MAs), the first visible symptom of diabetic retinopathy, and the other for multi-label detection of 28 ocular abnormalities consisting of frequent and rare abnormalities from a single CFP by using deep learning-based approaches. Two methods were proposed for MAs detection: ensemble-based and cascade-based methods. Ensemble-based MAs detection aims to find the best combination of input channels while the goal of cascade-based MAs detection is to reduce the false positive predictions with high sensitivity. The MAs detection with the cascade learning method achieves 0.792 sensitivity, the highest sensitivity on the E-Ophta dataset in 8 false positives per image. Two methods were also proposed for multi-label detection: Convolutional Neural Network (CNN)-based and Transformer-based methods. These proposed methods combine the visual features extracted from a color fundus image and the label co-occurrence dependencies extracted from linguistic features. The correlation between visual and linguistic features is learned by a semantic dictionary. CNN-based multi-label detection aims to adapt the model with out-of-vocabulary words. The results of this model show the positive impact of linguistic input interference in multi-label detection. Transformer-based multi-label detection enhances the linguistic input interference in multi-label detection. This method achieves a 0.804 final score, the highest score on the RFMiD Test dataset.

Download or read book Symbiotic Registration and Deep Learning for Retinal Image Analysis written by Li Ding (Electrical and computer engineering researcher) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Geometry and semantics are two sub-fields in computer vision that have been researched extensively as two separate problems over decades. However, semantics and geometry in computer vision are not mutually exclusive and techniques developed for one could complement the other. Unfortunately, the interplay of these two fields has received limited attention. In this thesis, we design symbiotic geometric and semantic computer vision methods in the specific context of retinal image analysis, where we consider the semantic problem of retinal vessel detection and the geometric problem of retinal image registration. First, we propose a novel pipeline for vessel detection in fluorescein angiography (FA) using deep neural networks (DNNs) that reduces the effort required for labeling ground truth data by combining cross-modality transfer and human-in-the-loop learning. The cross-modality transfer exploits concurrently captured color fundus (CF) and fundus FA. Binary vessels maps detected from CF images with a pre-trained network are geometrically registered with FA images via robust parametric chamfer alignment. Using the transferred vessels as initial ground truth labels, the human-in-the-loop approach progressively improves the ground truth labeling by iterating between deep-learning and labeling. Experiments show that the proposed pipeline significantly reduces the annotation effort and outperforms prior FA vessel detection methods by a significant margin. Next, we describe an annotation-efficient deep learning framework for vessel detection in UWF fundus photography (FP) that does not require de novo labeled UWF FP vessel maps. Our approach uses concurrently captured UWF FA and iterates between a multi-modal registration step and a weakly-supervised learning step. In the registration step, UWF FA vessel maps detected with a pre-trained DNN are registered with the UWF FP via parametric chamfer alignment. The warped vessel maps are used as the tentative training data but inevitably contain incorrect labels due to the differences between the two modalities and the errors in the registration. In the learning step, a robust learning method is proposed to train DNNs with noisy labels. The registration and the vessel detection benefit from each other and are progressively improved. Results on two datasets show that the proposed approach provides accurate vessel detection, without requiring manually labeled UWF FP training data. Finally, we present a hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in UWF FA. Our approach consists of a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies corresponding keypoints and estimates a polynomial geometric transformation consistent with the identified correspondences between reference and target images. Our RANSAC modification particularly improves feature matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The local refinement is formulated as a parametric chamfer alignment for vessel maps obtained using DNNs. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. Experiments conducted on two datasets show that the proposed framework significantly outperforms the existing retinal image registration methods"--Pages xv-xvii.

Download or read book MultiMedia Modeling written by Yong Man Ro and published by Springer Nature. This book was released on 2019-12-27 with total page 838 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 11961 and 11962 constitutes the thoroughly refereed proceedings of the 25th International Conference on MultiMedia Modeling, MMM 2020, held in Daejeon, South Korea, in January 2020. Of the 171 submitted full research papers, 40 papers were selected for oral presentation and 46 for poster presentation; 28 special session papers were selected for oral presentation and 8 for poster presentation; in addition, 9 demonstration papers and 6 papers for the Video Browser Showdown 2020 were accepted. The papers of LNCS 11961 are organized in the following topical sections: audio and signal processing; coding and HVS; color processing and art; detection and classification; face; image processing; learning and knowledge representation; video processing; poster papers; the papers of LNCS 11962 are organized in the following topical sections: poster papers; AI-powered 3D vision; multimedia analytics: perspectives, tools and applications; multimedia datasets for repeatable experimentation; multi-modal affective computing of large-scale multimedia data; multimedia and multimodal analytics in the medical domain and pervasive environments; intelligent multimedia security; demo papers; and VBS papers.

Download or read book Interdisciplinary techniques in biomedical photonics written by Wei Gong and published by Frontiers Media SA. This book was released on 2023-04-20 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Domain Adaptation in Computer Vision with Deep Learning written by Hemanth Venkateswara and published by Springer Nature. This book was released on 2020-08-18 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

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.