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Autor Dalca, Adrian |
Documentos disponibles escritos por este autor (4)
Crear una solicitud de compra Refinar búsquedaMolecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment / Cardoso, M. Jorge ; Arbel, Tal ; Gao, Fei ; Kainz, Bernhard ; van Walsum, Theo ; Shi, Kuangyu ; Bhatia, Kanwal K. ; Peter, Roman ; Vercauteren, Tom ; Reyes, Mauricio ; Dalca, Adrian ; Wiest, Roland ; Niessen, Wiro ; Emmer, Bart J.
TÃtulo : Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings Tipo de documento: documento electrónico Autores: Cardoso, M. Jorge, ; Arbel, Tal, ; Gao, Fei, ; Kainz, Bernhard, ; van Walsum, Theo, ; Shi, Kuangyu, ; Bhatia, Kanwal K., ; Peter, Roman, ; Vercauteren, Tom, ; Reyes, Mauricio, ; Dalca, Adrian, ; Wiest, Roland, ; Niessen, Wiro, ; Emmer, Bart J., Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2017 Número de páginas: XV, 186 p. 74 ilustraciones ISBN/ISSN/DL: 978-3-319-67564-0 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Visión por computador Sistemas de reconocimiento de patrones Inteligencia artificial Informática Médica Sistemas de almacenamiento y recuperación de información. Reconocimiento de patrones automatizado Informática de la Salud Almacenamiento y recuperación de información Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Taller internacional sobre métodos computacionales para imágenes moleculares, CMMI 2017, el Taller internacional sobre reconstrucción y análisis de órganos del cuerpo en movimiento, RAMBO 2017, y el Taller internacional sobre accidentes cerebrovasculares: desafÃos de imágenes y tratamiento, SWITCH 2017. celebrada junto con la 20.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2017, en la ciudad de Québec, QC, Canadá, en septiembre de 2017. Los 5 artÃculos completos presentados en FIFI 2017, los 9 artÃculos completos presentados en RAMBO 2017, y los 4 artÃculos completos presentados en SWITCH 2017 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de CMMI cubren diversas áreas, desde la sÃntesis de imágenes hasta el análisis de datos y desde el diagnóstico clÃnico hasta la individualización de la terapia, utilizando modalidades de imágenes moleculares PET, SPECT, PET/CT, SPECT/CT y PET/MR. Los artÃculos de RAMBO presentan investigaciones tanto del mundo académico como de la industria. Están organizados en las categorÃas "registro y seguimiento" y "reconstrucción de imágenes y recuperación de información", mientras que las áreas de aplicación incluyen imágenes cardÃacas, pulmonares, abdominales, fetales y renales. Los artÃculos de SWITCH se centran en biomarcadores de imágenes cuantitativos para accidentes cerebrovasculares basados ​​en CT(A). Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, the International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017, and the International Stroke Workshop: Imaging and Treatment Challenges, SWITCH 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 5 full papers presented at FIFI 2017, the 9 full papers presented at RAMBO 2017, and the 4 full papers presented at SWITCH 2017 were carefully reviewed and selected. The CMMI papers cover various areas from image synthesis to data analysis and from clinical diagnosis to therapy individualization, using molecular imaging modalities PET, SPECT, PET/CT, SPECT/CT, and PET/MR. The RAMBO papers present research from both academia and industry, They are organized into the categories "registration and tracking" and "image reconstruction and information retrieval" while application areas include cardiac, pulmonal, abdominal, fetal, and renal imaging. The SWITCH papers focus on CT(A)-based quantitative imaging biomarkers for stroke. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment : Fifth International Workshop, CMMI 2017, Second International Workshop, RAMBO 2017, and First International Workshop, SWITCH 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings [documento electrónico] / Cardoso, M. Jorge, ; Arbel, Tal, ; Gao, Fei, ; Kainz, Bernhard, ; van Walsum, Theo, ; Shi, Kuangyu, ; Bhatia, Kanwal K., ; Peter, Roman, ; Vercauteren, Tom, ; Reyes, Mauricio, ; Dalca, Adrian, ; Wiest, Roland, ; Niessen, Wiro, ; Emmer, Bart J., . - 1 ed. . - [s.l.] : Springer, 2017 . - XV, 186 p. 74 ilustraciones.
ISBN : 978-3-319-67564-0
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Visión por computador Sistemas de reconocimiento de patrones Inteligencia artificial Informática Médica Sistemas de almacenamiento y recuperación de información. Reconocimiento de patrones automatizado Informática de la Salud Almacenamiento y recuperación de información Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Taller internacional sobre métodos computacionales para imágenes moleculares, CMMI 2017, el Taller internacional sobre reconstrucción y análisis de órganos del cuerpo en movimiento, RAMBO 2017, y el Taller internacional sobre accidentes cerebrovasculares: desafÃos de imágenes y tratamiento, SWITCH 2017. celebrada junto con la 20.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2017, en la ciudad de Québec, QC, Canadá, en septiembre de 2017. Los 5 artÃculos completos presentados en FIFI 2017, los 9 artÃculos completos presentados en RAMBO 2017, y los 4 artÃculos completos presentados en SWITCH 2017 fueron cuidadosamente revisados ​​y seleccionados. Los artÃculos de CMMI cubren diversas áreas, desde la sÃntesis de imágenes hasta el análisis de datos y desde el diagnóstico clÃnico hasta la individualización de la terapia, utilizando modalidades de imágenes moleculares PET, SPECT, PET/CT, SPECT/CT y PET/MR. Los artÃculos de RAMBO presentan investigaciones tanto del mundo académico como de la industria. Están organizados en las categorÃas "registro y seguimiento" y "reconstrucción de imágenes y recuperación de información", mientras que las áreas de aplicación incluyen imágenes cardÃacas, pulmonares, abdominales, fetales y renales. Los artÃculos de SWITCH se centran en biomarcadores de imágenes cuantitativos para accidentes cerebrovasculares basados ​​en CT(A). Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the International Workshop on Computational Methods for Molecular Imaging, CMMI 2017, the International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2017, and the International Stroke Workshop: Imaging and Treatment Challenges, SWITCH 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 5 full papers presented at FIFI 2017, the 9 full papers presented at RAMBO 2017, and the 4 full papers presented at SWITCH 2017 were carefully reviewed and selected. The CMMI papers cover various areas from image synthesis to data analysis and from clinical diagnosis to therapy individualization, using molecular imaging modalities PET, SPECT, PET/CT, SPECT/CT, and PET/MR. The RAMBO papers present research from both academia and industry, They are organized into the categories "registration and tracking" and "image reconstruction and information retrieval" while application areas include cardiac, pulmonal, abdominal, fetal, and renal imaging. The SWITCH papers focus on CT(A)-based quantitative imaging biomarkers for stroke. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures : First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Tipo de documento: documento electrónico Autores: Greenspan, Hayit, ; Tanno, Ryutaro, ; Erdt, Marius, ; Arbel, Tal, ; Baumgartner, Christian, ; Dalca, Adrian, ; Sudre, Carole H., ; Wells, William M., ; Drechsler, Klaus, ; Linguraru, Marius George, ; Oyarzun Laura, Cristina, ; Shekhar, Raj, ; Wesarg, Stefan, ; González Ballester, Miguel Ãngel, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XVII, 192 p. 83 ilustraciones, 76 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-32689-0 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador Informática Médica Informática de la Salud Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Incertidumbre para la Utilización Segura del Aprendizaje Automático en Imágenes Médicas, UNSURE 2019, y el 8.º Taller Internacional sobre Procedimientos ClÃnicos Basados ​​en Imágenes, CLIP 2019, celebrado junto con MICCAI 2019, en Shenzhen. , China, en octubre de 2019. Para UNSURE 2019, se aceptaron para publicación 8 artÃculos de 15 presentaciones. Se centran en desarrollar conciencia y fomentar la investigación en el campo del modelado de incertidumbre para permitir la implementación segura de herramientas de aprendizaje automático en el mundo clÃnico. CLIP 2019 aceptó 11 artÃculos de las 15 presentaciones recibidas. Los talleres proporcionan un foro para el trabajo centrado en aplicaciones clÃnicas especÃficas, incluidas técnicas y procedimientos basados ​​en imágenes clÃnicas integrales y otros datos. . Nota de contenido: UNSURE 2019: Uncertainty quantification and noise modelling -- Probabilistic Surface Reconstruction with Unknown Correspondence -- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty -- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference -- Reg R-CNN: Lesion Detection and Grading under Noisy Labels -- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation -- Quantifying Uncertainty of deep neural networks in skin lesion classification -- UNSURE 2019: Domain shift robustness -- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data -- Out of distribution detection for intra-operative functional imaging -- CLIP 2019 -- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease -- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain -- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis -- Recovering physiological changes in nasal anatomy with confidence estimates -- Synthesis of Medical Images Using GANs -- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation -- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging -- Data Augmentation from Sketch -- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis -- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation -- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures : First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings [documento electrónico] / Greenspan, Hayit, ; Tanno, Ryutaro, ; Erdt, Marius, ; Arbel, Tal, ; Baumgartner, Christian, ; Dalca, Adrian, ; Sudre, Carole H., ; Wells, William M., ; Drechsler, Klaus, ; Linguraru, Marius George, ; Oyarzun Laura, Cristina, ; Shekhar, Raj, ; Wesarg, Stefan, ; González Ballester, Miguel Ãngel, . - 1 ed. . - [s.l.] : Springer, 2019 . - XVII, 192 p. 83 ilustraciones, 76 ilustraciones en color.
ISBN : 978-3-030-32689-0
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador Informática Médica Informática de la Salud Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Incertidumbre para la Utilización Segura del Aprendizaje Automático en Imágenes Médicas, UNSURE 2019, y el 8.º Taller Internacional sobre Procedimientos ClÃnicos Basados ​​en Imágenes, CLIP 2019, celebrado junto con MICCAI 2019, en Shenzhen. , China, en octubre de 2019. Para UNSURE 2019, se aceptaron para publicación 8 artÃculos de 15 presentaciones. Se centran en desarrollar conciencia y fomentar la investigación en el campo del modelado de incertidumbre para permitir la implementación segura de herramientas de aprendizaje automático en el mundo clÃnico. CLIP 2019 aceptó 11 artÃculos de las 15 presentaciones recibidas. Los talleres proporcionan un foro para el trabajo centrado en aplicaciones clÃnicas especÃficas, incluidas técnicas y procedimientos basados ​​en imágenes clÃnicas integrales y otros datos. . Nota de contenido: UNSURE 2019: Uncertainty quantification and noise modelling -- Probabilistic Surface Reconstruction with Unknown Correspondence -- Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertainty -- Propagating Uncertainty Across Cascaded Medical Imaging Tasks For Improved Deep Learning Inference -- Reg R-CNN: Lesion Detection and Grading under Noisy Labels -- Fast Nonparametric Mutual Information based Registration and Uncertainty Estimation -- Quantifying Uncertainty of deep neural networks in skin lesion classification -- UNSURE 2019: Domain shift robustness -- A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Data -- Out of distribution detection for intra-operative functional imaging -- CLIP 2019 -- A Clinical Measuring Platform for Building the Bridge across the Quantification of Pathological N-cells in Medical Imaging for Studies of Disease -- Spatiotemporal statistical model of anatomical landmarks on a human embryonic brain -- Spaciousness filters for non-contrast CT volume segmentation of the intestine region for emergency ileus diagnosis -- Recovering physiological changes in nasal anatomy with confidence estimates -- Synthesis of Medical Images Using GANs -- DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attention Modules for Colon Glands Segmentation -- Multi-instance deep learning with graph convolutional neural networks for diagnosis of kidney diseases using ultrasound imaging -- Data Augmentation from Sketch -- An automated CNN-based 3D anatomical landmark detection method to facilitate surface-based 3D facial shape analysis -- A Device-independent Novel Statistical Modeling for Cerebral TOF-MRA data Segmentation -- Three-dimensional face reconstruction from uncalibrated photographs: application to early detection of genetic syndromes. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings Tipo de documento: documento electrónico Autores: Sudre, Carole H., ; Fehri, Hamid, ; Arbel, Tal, ; Baumgartner, Christian F., ; Dalca, Adrian, ; Tanno, Ryutaro, ; Van Leemput, Koen, ; Wells, William M., ; Sotiras, Aristeidis, ; Papiez, Bartlomiej, ; Ferrante, Enzo, ; Parisot, Sarah, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XVII, 222 p. 85 ilustraciones, 76 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-60365-6 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Sistemas de reconocimiento de patrones Visión por computador Ciencias sociales Reconocimiento de patrones automatizado Aplicación informática en ciencias sociales y del comportamiento. Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Segundo Taller Internacional sobre Incertidumbre para la Utilización Segura del Aprendizaje Automático en Imágenes Médicas, UNSURE 2020, y el Tercer Taller Internacional sobre Gráficos en Análisis de Imágenes Biomédicas, GRAIL 2020, celebrado en conjunto con MICCAI 2020, en Lima. , Perú, en octubre de 2020. Los talleres se realizaron de manera virtual debido a la pandemia de COVID-19. Para UNSURE 2020, se aceptó para publicación 10 artÃculos de 18 presentaciones. Se centran en desarrollar conciencia y fomentar la investigación en el campo del modelado de incertidumbre para permitir la implementación segura de herramientas de aprendizaje automático en el mundo clÃnico. GRAIL 2020 aceptó 10 artÃculos de las 12 presentaciones recibidas. El taller tiene como objetivo reunir a cientÃficos que utilizan y desarrollan modelos basados ​​en gráficos para el análisis de imágenes biomédicas y fomentar la exploración de modelos basados ​​en gráficos para problemas clÃnicos difÃciles dentro de una variedad de contextos de imágenes biomédicas. Nota de contenido: UNSURE 2020 -- Image registration via stochastic gradient Markov chain Monte Carlo -- RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification -- Hierarchical brain parcellation with uncertainty -- Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-Class Segmentation -- Uncertainty Estimation in Landmark Localization based on Gaussian Heatmaps -- Weight averaging impact on the uncertainty of retinal artery-venous segmentation -- Improving Pathological Distribution Measurements with Bayesian Uncertainty -- Improving Reliability of Clinical Models using Prediction Calibration -- Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior -- Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability -- GRAIL 2020 -- Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates -- Detection of Discriminative Neurological Circuits Using Hierarchical GraphConvolutional Networks in fMRI Sequences -- Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders -- Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Proling -- Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation -- Min-cut Max-flow for Network Abnormality Detection: Application to Preterm Birth -- Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface -- The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification -- Intraoperative Liver Surface Completion with Graph Convolutional VAE -- HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis : Second International Workshop, UNSURE 2020, and Third International Workshop, GRAIL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings [documento electrónico] / Sudre, Carole H., ; Fehri, Hamid, ; Arbel, Tal, ; Baumgartner, Christian F., ; Dalca, Adrian, ; Tanno, Ryutaro, ; Van Leemput, Koen, ; Wells, William M., ; Sotiras, Aristeidis, ; Papiez, Bartlomiej, ; Ferrante, Enzo, ; Parisot, Sarah, . - 1 ed. . - [s.l.] : Springer, 2020 . - XVII, 222 p. 85 ilustraciones, 76 ilustraciones en color.
ISBN : 978-3-030-60365-6
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Sistemas de reconocimiento de patrones Visión por computador Ciencias sociales Reconocimiento de patrones automatizado Aplicación informática en ciencias sociales y del comportamiento. Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Segundo Taller Internacional sobre Incertidumbre para la Utilización Segura del Aprendizaje Automático en Imágenes Médicas, UNSURE 2020, y el Tercer Taller Internacional sobre Gráficos en Análisis de Imágenes Biomédicas, GRAIL 2020, celebrado en conjunto con MICCAI 2020, en Lima. , Perú, en octubre de 2020. Los talleres se realizaron de manera virtual debido a la pandemia de COVID-19. Para UNSURE 2020, se aceptó para publicación 10 artÃculos de 18 presentaciones. Se centran en desarrollar conciencia y fomentar la investigación en el campo del modelado de incertidumbre para permitir la implementación segura de herramientas de aprendizaje automático en el mundo clÃnico. GRAIL 2020 aceptó 10 artÃculos de las 12 presentaciones recibidas. El taller tiene como objetivo reunir a cientÃficos que utilizan y desarrollan modelos basados ​​en gráficos para el análisis de imágenes biomédicas y fomentar la exploración de modelos basados ​​en gráficos para problemas clÃnicos difÃciles dentro de una variedad de contextos de imágenes biomédicas. Nota de contenido: UNSURE 2020 -- Image registration via stochastic gradient Markov chain Monte Carlo -- RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification -- Hierarchical brain parcellation with uncertainty -- Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-Class Segmentation -- Uncertainty Estimation in Landmark Localization based on Gaussian Heatmaps -- Weight averaging impact on the uncertainty of retinal artery-venous segmentation -- Improving Pathological Distribution Measurements with Bayesian Uncertainty -- Improving Reliability of Clinical Models using Prediction Calibration -- Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior -- Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability -- GRAIL 2020 -- Clustering-based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates -- Detection of Discriminative Neurological Circuits Using Hierarchical GraphConvolutional Networks in fMRI Sequences -- Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders -- Multi-Scale Profiling of Brain Multigraphs by Eigen-based Cross-Diffusion and Heat Tracing for Brain State Proling -- Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation -- Min-cut Max-flow for Network Abnormality Detection: Application to Preterm Birth -- Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface -- The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification -- Intraoperative Liver Surface Completion with Graph Convolutional VAE -- HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis : 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings Tipo de documento: documento electrónico Autores: Sudre, Carole H., ; Licandro, Roxane, ; Baumgartner, Christian, ; Melbourne, Andrew, ; Dalca, Adrian, ; Hutter, Jana, ; Tanno, Ryutaro, ; Abaci Turk, Esra, ; Van Leemput, Koen, ; Torrents Barrena, Jordina, ; Wells, William M., ; Macgowan, Christopher, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XIII, 296 p. 112 ilustraciones, 103 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-87735-4 Nota general: Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. Idioma : Inglés (eng) Palabras clave: Inteligencia artificial Visión por computador Bioinformática Sistemas de reconocimiento de patrones BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Tercer Taller Internacional sobre Incertidumbre para la Utilización Segura del Aprendizaje Automático en Imágenes Médicas, UNSURE 2021, y el 6to Taller Internacional sobre Análisis de Imágenes Prematuros, Perinatales y Pediátricas, PIPPI 2021, celebrado junto con MICCAI 2021. Estaba previsto que la conferencia se celebrara en Estrasburgo, Francia, pero se celebró virtualmente debido a la pandemia de COVID-19. Para UNSURE 2021, se aceptó para publicación 13 artÃculos de 18 presentaciones. Se centran en desarrollar conciencia y fomentar la investigación en el campo del modelado de incertidumbre para permitir la implementación segura de herramientas de aprendizaje automático en el mundo clÃnico. PIPPI 2021 aceptó 14 artÃculos de las 18 presentaciones recibidas. El taller tiene como objetivo reunir métodos y experiencias de investigadores y autores que trabajan en estas cohortes más jóvenes y proporciona un foro para la discusión abierta sobre enfoques avanzados de análisis de imágenes centrados en el análisis del crecimiento y desarrollo en el perÃodo fetal, infantil y pediátrico. Nota de contenido: UNSURE 2021 - Uncertainty estimation and modelling and annotation uncertainty -- Model uncertainty estimation for medical Imaging based diagnosis -- Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic -- Leveraging uncertainty estimates to improve segmentation performance in cardiac MR -- Improving the reliability of semantic segmentation of medical images by uncertainty modelling with Bayesian deep networks and curriculum learning -- Unpaired MR image homogeneisation by disentangled representations and its uncertainty -- Uncertainty-aware deep learning based deformable registration -- Monte Carlo Concrete DropPath for Epistemic Uncertainty Estimation in Brain Tumour segmentation -- Improving Aleatoric Uncertainty quantification in multi-annotated medical image segmentation with normalizing flows -- UNSURE 2021 – Domain shift robustness and risk management in clinical pipelines -- Task-agnostic out-of-distribution detection using kernel density estimation -- Out of distribution detection for medical images -- Robust selective classification of skin lesions with asymmetric costs -- Confidence-based Out-of-Distribution detection: a comparative study and analysis -- Novel disease detection using ensembles with regularized disagreement -- PIPPI2021 -- Automatic Placenta Abnormality Detection using Convolutional Neural Networks on Ultrasound Texture -- Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction -- Spatio-temporal atlas of normal fetal craniofacial feature development and CNN-based ocular biometry for motion-corrected fetal MRI -- Myelination of preterm brain networks at adolescence -- A bootstrap self-training method for sequence transfer: State-of-the-art placenta segmentation in fetal MRI -- Segmentation of the cortical plate in fetal brain MRI with a topological loss -- Fetal brain MRI measurements using a deep learning landmark network with reliability estimation -- CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI -- Detection of Injury and Automated Triage of Preterm Neonatal MRI using Patch-Based Gaussian Processes -- Assessment of Regional Cortical Development through Fissure Based Gestational Age Estimation in 3D Fetal Ultrasound -- Texture-based Analysis of Fetal Organs in Fetal Growth Restriction -- Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI -- Analysis of the Anatomical Variability of Fetal Brains with Corpus Callosum Agenesis -- Predicting preterm birth using multimodal fetal imaging. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Third International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis : 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings [documento electrónico] / Sudre, Carole H., ; Licandro, Roxane, ; Baumgartner, Christian, ; Melbourne, Andrew, ; Dalca, Adrian, ; Hutter, Jana, ; Tanno, Ryutaro, ; Abaci Turk, Esra, ; Van Leemput, Koen, ; Torrents Barrena, Jordina, ; Wells, William M., ; Macgowan, Christopher, . - 1 ed. . - [s.l.] : Springer, 2021 . - XIII, 296 p. 112 ilustraciones, 103 ilustraciones en color.
ISBN : 978-3-030-87735-4
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Idioma : Inglés (eng)
Palabras clave: Inteligencia artificial Visión por computador Bioinformática Sistemas de reconocimiento de patrones BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Tercer Taller Internacional sobre Incertidumbre para la Utilización Segura del Aprendizaje Automático en Imágenes Médicas, UNSURE 2021, y el 6to Taller Internacional sobre Análisis de Imágenes Prematuros, Perinatales y Pediátricas, PIPPI 2021, celebrado junto con MICCAI 2021. Estaba previsto que la conferencia se celebrara en Estrasburgo, Francia, pero se celebró virtualmente debido a la pandemia de COVID-19. Para UNSURE 2021, se aceptó para publicación 13 artÃculos de 18 presentaciones. Se centran en desarrollar conciencia y fomentar la investigación en el campo del modelado de incertidumbre para permitir la implementación segura de herramientas de aprendizaje automático en el mundo clÃnico. PIPPI 2021 aceptó 14 artÃculos de las 18 presentaciones recibidas. El taller tiene como objetivo reunir métodos y experiencias de investigadores y autores que trabajan en estas cohortes más jóvenes y proporciona un foro para la discusión abierta sobre enfoques avanzados de análisis de imágenes centrados en el análisis del crecimiento y desarrollo en el perÃodo fetal, infantil y pediátrico. Nota de contenido: UNSURE 2021 - Uncertainty estimation and modelling and annotation uncertainty -- Model uncertainty estimation for medical Imaging based diagnosis -- Accurate simulation of operating system updates in neuroimaging using Monte-Carlo arithmetic -- Leveraging uncertainty estimates to improve segmentation performance in cardiac MR -- Improving the reliability of semantic segmentation of medical images by uncertainty modelling with Bayesian deep networks and curriculum learning -- Unpaired MR image homogeneisation by disentangled representations and its uncertainty -- Uncertainty-aware deep learning based deformable registration -- Monte Carlo Concrete DropPath for Epistemic Uncertainty Estimation in Brain Tumour segmentation -- Improving Aleatoric Uncertainty quantification in multi-annotated medical image segmentation with normalizing flows -- UNSURE 2021 – Domain shift robustness and risk management in clinical pipelines -- Task-agnostic out-of-distribution detection using kernel density estimation -- Out of distribution detection for medical images -- Robust selective classification of skin lesions with asymmetric costs -- Confidence-based Out-of-Distribution detection: a comparative study and analysis -- Novel disease detection using ensembles with regularized disagreement -- PIPPI2021 -- Automatic Placenta Abnormality Detection using Convolutional Neural Networks on Ultrasound Texture -- Simulated Half-Fourier Acquisitions Single-shot Turbo Spin Echo (HASTE) of the Fetal Brain: Application to Super-Resolution Reconstruction -- Spatio-temporal atlas of normal fetal craniofacial feature development and CNN-based ocular biometry for motion-corrected fetal MRI -- Myelination of preterm brain networks at adolescence -- A bootstrap self-training method for sequence transfer: State-of-the-art placenta segmentation in fetal MRI -- Segmentation of the cortical plate in fetal brain MRI with a topological loss -- Fetal brain MRI measurements using a deep learning landmark network with reliability estimation -- CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI -- Detection of Injury and Automated Triage of Preterm Neonatal MRI using Patch-Based Gaussian Processes -- Assessment of Regional Cortical Development through Fissure Based Gestational Age Estimation in 3D Fetal Ultrasound -- Texture-based Analysis of Fetal Organs in Fetal Growth Restriction -- Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI -- Analysis of the Anatomical Variability of Fetal Brains with Corpus Callosum Agenesis -- Predicting preterm birth using multimodal fetal imaging. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the Third International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
