S. Kevin Zhou – författare

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31 produkter

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31 produkter

Medical Image Recognition, Segmentation and Parsing
Machine Learning and Multiple Object Approaches
AvS. Kevin Zhou
Inbunden, Engelska, 2015
1 391 kr
Skickas inom 10-15 vardagar
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.

Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.

Learn:

Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects Methods and theories for medical image recognition, segmentation and parsing of multiple objects Efficient and effective machine learning solutions based on big datasets Selected applications of medical image parsing using proven algorithms Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets Includes algorithms for recognizing and parsing of known anatomies for practical applications
Medical Image Recognition, Segmentation and Parsing
Machine Learning and Multiple Object Approaches
AvS. Kevin Zhou
E-bok
Engelska, 2015
1 579 kr
Läs direkt efter köp
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.Learn:- Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects- Methods and theories for medical image recognition, segmentation and parsing of multiple objects- Efficient and effective machine learning solutions based on big datasets- Selected applications of medical image parsing using proven algorithms- Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects- Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets- Includes algorithms for recognizing and parsing of known anatomies for practical applications
Deep Learning for Medical Image Analysis
AvS. Kevin Zhou,S. Kevin Zhoum. fl.
Häftad, Engelska, 2017
1 274 kr
Skickas inom 10-15 vardagar
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
Deep Learning for Medical Image Analysis
AvDinggang Shen,Hayit Greenspanm. fl.
E-bok
Engelska, 2017
1 364 kr
Läs direkt efter köp
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
Handbook of Medical Image Computing and Computer Assisted Intervention
AvS. Kevin Zhou,Daniel Rueckertm. fl.
Inbunden, Engelska, 2019
2 283 kr
Skickas inom 10-15 vardagar
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.
Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention
Handbook of Medical Image Computing and Computer Assisted Intervention
AvGabor Fichtinger,Daniel Rueckertm. fl.
E-bok
Engelska, 2019
3 199 kr
Läs direkt efter köp
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.- Presents the key research challenges in medical image computing and computer-assisted intervention- Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society- Contains state-of-the-art technical approaches to key challenges- Demonstrates proven algorithms for a whole range of essential medical imaging applications- Includes source codes for use in a plug-and-play manner- Embraces future directions in the fields of medical image computing and computer-assisted intervention
Deep Network Design for Medical Image Computing
Principles and Applications
AvHaofu Liao,S. Kevin Zhoum. fl.
Häftad, Engelska, 2022
1 106 kr
Skickas inom 10-15 vardagar
Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more.

This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems.
Explains design principles of deep learning techniques for MIC Contains cutting-edge deep learning research on MIC Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images
Deep Network Design for Medical Image Computing
Principles and Applications
AvJiebo Luo,S. Kevin Zhoum. fl.
E-bok
Engelska, 2022
1 570 kr
Läs direkt efter köp
Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design principles of these tasks for deep learning approaches in medicine. These include skin disease classification, vertebrae identification and localization, cardiac ultrasound image segmentation, 2D/3D medical image registration for intervention, metal artifact reduction, sparse-view artifact reduction, etc. For each topic, the book provides a deep learning-based solution that takes into account the medical or biological aspect of the problem and how the solution addresses a variety of important questions surrounding architecture, the design of deep learning techniques, when to introduce adversarial learning, and more.This book will help graduate students and researchers develop a better understanding of the deep learning design principles for MIC and to apply them to their medical problems.- Explains design principles of deep learning techniques for MIC- Contains cutting-edge deep learning research on MIC- Covers a broad range of MIC tasks, including the classification, detection, segmentation, registration, reconstruction and synthesis of medical images
Deep Learning for Medical Image Analysis
AvS. Kevin Zhou,Hayit Greenspanm. fl.
Häftad, Engelska, 2023
1 252 kr
Skickas inom 10-15 vardagar
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 challengesDescribes the latest deep learning methods and the theories behind approaches for medical image analysisTeaches 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 Medical Image Analysis
AvDinggang Shen,Hayit Greenspanm. fl.
E-bok
Engelska, 2023
1 847 kr
Läs direkt efter köp
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 Covid Image Analysis
AvHayit Greenspan,S. Kevin Zhou
Häftad, Engelska, 2021
1 248 kr
Kommande
Del 12261 - Lecture Notes in Computer Science
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I
AvAnne L. Martel,Purang Abolmaesumim. fl.
Häftad, Engelska, 2020
1 082 kr
Skickas inom 10-15 vardagar
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 methodologiesPart II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networksPart III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysisPart IV: segmentation; shape models and landmark detectionPart V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmologyPart VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imagingPart VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I
AvLeo Joskowicz,Daniel Racoceanum. fl.
E-bok
Engelska, 2020
1 413 kr
Läs direkt efter köp
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
Del 12262 - Lecture Notes in Computer Science
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II
AvAnne L. Martel,Purang Abolmaesumim. fl.
Häftad, Engelska, 2020
1 082 kr
Skickas inom 10-15 vardagar
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 methodologiesPart II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networksPart III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysisPart IV: segmentation; shape models and landmark detectionPart V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmologyPart VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imagingPart VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II
AvLeo Joskowicz,Daniel Racoceanum. fl.
E-bok
Engelska, 2020
1 413 kr
Läs direkt efter köp
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
Del 12263 - Lecture Notes in Computer Science
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III
AvAnne L. Martel,Purang Abolmaesumim. fl.
Häftad, Engelska, 2020
1 113 kr
Skickas inom 10-15 vardagar
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 methodologiesPart II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networksPart III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysisPart IV: segmentation; shape models and landmark detectionPart V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmologyPart VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imagingPart VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III
AvLeo Joskowicz,Daniel Racoceanum. fl.
E-bok
Engelska, 2020
1 427 kr
Läs direkt efter köp
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
Del 12264 - Lecture Notes in Computer Science
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV
AvAnne L. Martel,Purang Abolmaesumim. fl.
Häftad, Engelska, 2020
1 512 kr
Skickas inom 10-15 vardagar
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 methodologiesPart II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networksPart III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysisPart IV: segmentation; shape models and landmark detectionPart V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmologyPart VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imagingPart VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV
AvLeo Joskowicz,Daniel Racoceanum. fl.
E-bok
Engelska, 2020
1 886 kr
Läs direkt efter köp
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
Del 12265 - Lecture Notes in Computer Science
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V
AvAnne L. Martel,Purang Abolmaesumim. fl.
Häftad, Engelska, 2020
1 113 kr
Skickas inom 10-15 vardagar
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 methodologiesPart II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networksPart III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysisPart IV: segmentation; shape models and landmark detectionPart V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmologyPart VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imagingPart VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part V
AvLeo Joskowicz,Daniel Racoceanum. fl.
E-bok
Engelska, 2020
1 373 kr
Läs direkt efter köp
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
Del 12266 - Lecture Notes in Computer Science
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI
AvAnne L. Martel,Purang Abolmaesumim. fl.
Häftad, Engelska, 2020
1 512 kr
Skickas inom 10-15 vardagar
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 methodologiesPart II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networksPart III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysisPart IV: segmentation; shape models and landmark detectionPart V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmologyPart VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imagingPart VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI
AvLeo Joskowicz,Daniel Racoceanum. fl.
E-bok
Engelska, 2020
1 906 kr
Läs direkt efter köp
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
Del 12267 - Lecture Notes in Computer Science
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII
AvAnne L. Martel,Purang Abolmaesumim. fl.
Häftad, Engelska, 2020
1 082 kr
Skickas inom 10-15 vardagar
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 methodologiesPart II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networksPart III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysisPart IV: segmentation; shape models and landmark detectionPart V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmologyPart VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imagingPart VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Medical Image Computing and Computer Assisted Intervention - MICCAI 2020
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII
AvLeo Joskowicz,Daniel Racoceanum. fl.
E-bok
Engelska, 2020
1 387 kr
Läs direkt efter köp
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
Recognition of Humans and Their Activities Using Video
AvRama Chellappa,Amit K. Roy-Chowdhurym. fl.
Häftad, Engelska, 2007
317 kr
Skickas inom 10-15 vardagar
The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusionof different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.
Recognition of Humans and Their Activities Using Video
AvS. Kevin Zhou,Amit K. Roy-Chowdhurym. fl.
E-bok
PDF, Engelska, 2022
399 kr
Läs direkt efter köp
The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusionof different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.
Del 16146 - Lecture Notes in Computer Science
Emerging LLM/LMM Applications in Medical Imaging
First International Workshop, ELAMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings
AvHien Van Nguyen,Akash Awasthim. fl.
Häftad, Engelska, 2025
850 kr
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This book constitutes the proceedings of the First International Workshop on Emerging LLM/LMM Applications in Medical Imaging, ELAMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025. The 14 full papers were carefully reviewed and selected from 24 submissions. The conference covers all aspects of Healthcare, LLM, LMM, Medical Imaging, AI, Machine learning, and Multimodal.
Emerging LLM/LMM Applications in Medical Imaging
First International Workshop, ELAMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings
AvS. Kevin Zhou,Sheng Lium. fl.
E-bok
Engelska, 2025
1 067 kr
Läs direkt efter köp
This book constitutes the proceedings of the First International Workshop on Emerging LLM/LMM Applications in Medical Imaging, ELAMI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025. The 14 full papers were carefully reviewed and selected from 24 submissions. The conference covers all aspects of Healthcare, LLM, LMM, Medical Imaging, AI, Machine learning, and Multimodal.
Analysis and Modeling of Faces and Gestures
Third International Workshop, AMFG 2007 Rio de Janeiro, Brazil, October 20, 2007 Proceedings
AvS. Kevin Zhou,Wen-Yi Zhaom. fl.
Häftad, Engelska, 2007
577 kr
Skickas inom 10-15 vardagar
The 2007 IEEE International Workshop on Analysis and Modeling of Faces and Gestures(AMFG)isthethirdworkshopofitstypeorganizedinconjunctionwith ICCV, this time in Rio de Janeiro, Brazil. Our primary goal is to bring together researchersand research groupsto review the status of recognition, analysis and modeling of face, gesture, activity, and behavior; to discuss the challenges that we are facing; and to explore future directions. This year we received 55 submissions. Each paper was reviewed by three program committee members. The whole reviewing process was double blind. However,due to size limit, we were only able to accommodate 22 papers, among which 8 are orals and 14 are posters. The topics covered by these accepted papers include feature representation, 3D face, robust recognition under pose and illumination variations,video-basedface recognition,learning,facial motion analysis, body pose estimation, and sign recognition. A special word of thanks goes to Dr. Feng Zhao, our organizing chair, for his dedication and great e?orts in maintaining both the online submission system and workshop website and in handling most of the author contacts. We are indebted to the advisory committee members for their valuable suggestions and to the program committee members for their hard work and timely reviews. Finally, we thank Cognitec System GmbH and Siemens Corporate Research for their sponsorship. October 2007 S. Kevin Zhou Wen-Yi Zhao Xiaoou Tang Shaogang Gong Organization AMFG 2007 was held in conjunction with ICCV 2007. Workshop Chairs S. Kevin Zhou Siemens Corporate Research Wen-Yi Zhao Intuitive Surgical, Inc.

S. Kevin Zhou – författare

Medical Image Recognition, Segmentation and Parsing

Medical Image Recognition, Segmentation and Parsing

Deep Learning for Medical Image Analysis

Deep Learning for Medical Image Analysis

Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention

Deep Network Design for Medical Image Computing

Deep Network Design for Medical Image Computing

Deep Learning for Medical Image Analysis

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Medical Image Computing and Computer Assisted Intervention - MICCAI 2020

Recognition of Humans and Their Activities Using Video

Recognition of Humans and Their Activities Using Video

Emerging LLM/LMM Applications in Medical Imaging

Emerging LLM/LMM Applications in Medical Imaging

Analysis and Modeling of Faces and Gestures