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This volume combines the established expertise of leaders in this diverse arena with practical applications of this transformative platform while harnessing a content rich, interactive format. In detailing a cohesive narrative from a broad, global perspective the lessons learned from the past, the obstacles to digital pathology adoption that have been overcome and the challenges that remain for full realization of the potential that computational analysis affords, this text provides readers with the latest in where the field is heading as it seeks to unlock the potential of digital pathology by leveraging cutting edge technologies and innovative tools. Digital Pathology: Historical Perspectives, Current Concepts & Future Applications will be of great value to Pathologists including Translational Researchers and Informaticists, Pathology Trainees including Residents and Informatics Fellows, Healthcare CMIOs and CIOs, Bioimaging Engineers and Computer Vision Scientists, as well as Laboratory Information Technologists.
Pathology --- computervisie --- pathologie
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This unique text/reference presents a detailed review of noise removal for photographs and video. An international selection of expert contributors provide their insights into the fundamental challenges that remain in the field of denoising, examining how to properly model noise in real scenarios, how to tailor denoising algorithms to these models, and how to evaluate the results in a way that is consistent with perceived image quality. The book offers comprehensive coverage from problem formulation to the evaluation of denoising methods, from historical perspectives to state-of-the-art algorithms, and from fast real-time techniques that can be implemented in-camera to powerful and computationally intensive methods for off-line processing. Topics and features: Describes the basic methods for the analysis of signal-dependent and correlated noise, and the key concepts underlying sparsity-based image denoising algorithms Reviews the most successful variational approaches for image reconstruction, and introduces convolutional neural network-based denoising methods Provides an overview of the use of Gaussian priors for patch-based image denoising, and examines the potential of internal denoising Discusses selection and estimation strategies for patch-based video denoising, and explores how noise enters the imaging pipeline Surveys the properties of real camera noise, and outlines a fast approximation of nonlocal means filtering Proposes routes to improving denoising results via indirectly denoising a transform of the image, considering the right noise model and taking into account the perceived quality of the outputs This concise and clearly written volume will be of great value to researchers and professionals working in image processing and computer vision. The book will also serve as an accessible reference for advanced undergraduate and graduate students in computer science, applied mathematics, and related fields. Marcelo Bertalmío is a Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra, Barcelona, Spain.
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This book constitutes the proceedings of the 27th International Conference on Information Processing in Medical Imaging, IPMI 2021, which was held online during June 28-30, 2021. The conference was originally planned to take place in Bornholm, Denmark, but changed to a virtual format due to the COVID-19 pandemic. The 59 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows: registration; causal models and interpretability; generative modelling; shape; brain connectivity; representation learning; segmentation; sequential modelling; learning with few or low quality labels; uncertainty quantification and generative modelling; and deep learning.
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The two volume set LNCS 13052 and 13053 constitutes the refereed proceedings of the 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, held virtually, in September 2021. The 87 papers presented were carefully reviewed and selected from 129 submissions. The papers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration.
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This book constitutes the refereed proceedings of the Third International Symposium on Mathematical and Computational Oncology, ISMCO 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 3 full papers and 4 short papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: statistical and machine learning methods for cancer research; mathematical modeling for cancer research; spatio-temporal tumor modeling and simulation; general cancer computational biology; mathematical modeling for cancer research; computational methods for anticancer drug development.
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Mathematics --- computervisie --- camera's
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