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Autor Zhou, Luping |
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TÃtulo : Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings Tipo de documento: documento electrónico Autores: Zhang, Daoqiang, ; Zhou, Luping, ; Jie, Biao, ; Liu, Mingxia, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: IX, 182 p. 87 ilustraciones, 68 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-35817-4 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: Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento. Reconocimiento de patrones automatizado Visión por computador Inteligencia artificial Ciencias sociales Procesamiento de datos Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Aprendizaje de Gráficos en Imágenes Médicas, GLMI 2019, celebrado junto con MICCAI 2019 en Shenzhen, China, en octubre de 2019. Los 21 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 42 presentaciones. Los artÃculos se centran en las principales tendencias y desafÃos del aprendizaje de gráficos en imágenes médicas y presentan trabajos originales destinados a identificar nuevas técnicas de vanguardia y sus aplicaciones en imágenes médicas. Nota de contenido: Graph Hyperalignment for Multi-Subject fMRI Functional Alignment -- Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks -- Adaptive Thresholding of Functional Connectivity Networks for fMRI-based Brain Disease Analysis -- Graph-kernel-based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification -- Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation -- Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction -- Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients -- Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography -- Triplet Graph Convolutional Network forMulti-scale Analysis of Functional Connectivityusing Functional MRI -- Multi-Scale Graph Convolutional Network for Mild Cognitive Impairment Detection -- DeepBundle: Fiber Bundle Parcellation With Graph CNNs -- Identification of Functional Connectivity Features in Depression Subtypes Using a Data-Driven Approach -- Movie-watching fMRI Reveals Inter-subject Synchrony Alteration in Functional Brain Activity in ADHD -- Weakly- and Semi- Supervised Graph CNN for identifying Basal Cell Carcinoma on Pathological images -- Geometric Brain Surface Network For Brain Cortical Parcellation -- Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images using 3D Mask R-CNN -- Discriminative-Region-Aware Residual Network for Adolescent Brain Structure and Cognitive Development Analysis -- Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram -- OCD Diagnosis via Smoothing Sparse Network and Stacked Sparse Auto-Encoder Learning -- A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism -- CNS: CycleGAN-assisted Neonatal Segmentation Model for Cross-Datasets. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Graph Learning in Medical Imaging : First International Workshop, GLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings [documento electrónico] / Zhang, Daoqiang, ; Zhou, Luping, ; Jie, Biao, ; Liu, Mingxia, . - 1 ed. . - [s.l.] : Springer, 2019 . - IX, 182 p. 87 ilustraciones, 68 ilustraciones en color.
ISBN : 978-3-030-35817-4
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: Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento. Reconocimiento de patrones automatizado Visión por computador Inteligencia artificial Ciencias sociales Procesamiento de datos Clasificación: 006.3 Resumen: Este libro constituye las actas arbitradas del Primer Taller Internacional sobre Aprendizaje de Gráficos en Imágenes Médicas, GLMI 2019, celebrado junto con MICCAI 2019 en Shenzhen, China, en octubre de 2019. Los 21 artÃculos completos presentados fueron cuidadosamente revisados ​​y seleccionados entre 42 presentaciones. Los artÃculos se centran en las principales tendencias y desafÃos del aprendizaje de gráficos en imágenes médicas y presentan trabajos originales destinados a identificar nuevas técnicas de vanguardia y sus aplicaciones en imágenes médicas. Nota de contenido: Graph Hyperalignment for Multi-Subject fMRI Functional Alignment -- Interactive 3D Segmentation Editing and Refinement via Gated Graph Neural Networks -- Adaptive Thresholding of Functional Connectivity Networks for fMRI-based Brain Disease Analysis -- Graph-kernel-based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification -- Linking convolutional neural networks with graph convolutional networks: application in pulmonary artery-vein separation -- Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction -- Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients -- Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography -- Triplet Graph Convolutional Network forMulti-scale Analysis of Functional Connectivityusing Functional MRI -- Multi-Scale Graph Convolutional Network for Mild Cognitive Impairment Detection -- DeepBundle: Fiber Bundle Parcellation With Graph CNNs -- Identification of Functional Connectivity Features in Depression Subtypes Using a Data-Driven Approach -- Movie-watching fMRI Reveals Inter-subject Synchrony Alteration in Functional Brain Activity in ADHD -- Weakly- and Semi- Supervised Graph CNN for identifying Basal Cell Carcinoma on Pathological images -- Geometric Brain Surface Network For Brain Cortical Parcellation -- Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images using 3D Mask R-CNN -- Discriminative-Region-Aware Residual Network for Adolescent Brain Structure and Cognitive Development Analysis -- Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram -- OCD Diagnosis via Smoothing Sparse Network and Stacked Sparse Auto-Encoder Learning -- A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism -- CNS: CycleGAN-assisted Neonatal Segmentation Model for Cross-Datasets. Tipo de medio : Computadora Summary : This book constitutes the refereed proceedings of the First International Workshop on Graph Learning in Medical Imaging, GLMI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 21 full papers presented were carefully reviewed and selected from 42 submissions. The papers focus on major trends and challenges of graph learning in medical imaging and present original work aimed to identify new cutting-edge techniques and their applications in medical imaging. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention / Zhou, Luping ; Heller, Nicholas ; Shi, Yiyu ; Xiao, Yiming ; Sznitman, Raphael ; Cheplygina, Veronika ; Mateus, Diana ; Trucco, Emanuele ; Hu, X. Sharon ; Chen, Danny ; Chabanas, Matthieu ; Rivaz, Hassan ; Reinertsen, Ingerid
TÃtulo : Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings Tipo de documento: documento electrónico Autores: Zhou, Luping, ; Heller, Nicholas, ; Shi, Yiyu, ; Xiao, Yiming, ; Sznitman, Raphael, ; Cheplygina, Veronika, ; Mateus, Diana, ; Trucco, Emanuele, ; Hu, X. Sharon, ; Chen, Danny, ; Chabanas, Matthieu, ; Rivaz, Hassan, ; Reinertsen, Ingerid, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XX, 154 p. 62 ilustraciones, 48 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-33642-4 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: Visión por computador Inteligencia artificial Informática Médica Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Cuarto Taller Internacional sobre Anotación a Gran Escala de Datos Biomédicos y SÃntesis de Etiquetas Expertas, LABELS 2019, el Primer Taller Internacional sobre Aprendizaje Consciente de Hardware para Imágenes Médicas e Intervención Asistida por Computadora, HAL-MICCAI 2019, y el Segundo Taller Internacional sobre Corrección del Cambio Cerebral con Ultrasonido Intraoperatorio, CuRIOUS 2019, celebrado junto con la 22.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 8 artÃculos presentados En LABELS 2019, los 5 artÃculos presentados en HAL-MICCAI 2019 y los 3 artÃculos presentados en CuRIOUS 2019 fueron cuidadosamente revisados ​​y seleccionados entre numerosas presentaciones. Los artÃculos de LABELS presentan una variedad de enfoques para abordar un número limitado de etiquetas, desde el aprendizaje semisupervisado hasta el crowdsourcing. Los artÃculos de HAL-MICCAI cubren un amplio conjunto de aplicaciones de hardware en problemas médicos, incluida la segmentación de imágenes médicas, la tomografÃa electrónica, la detección de neumonÃa, etc. Los artÃculos de CuRIOUS brindan una instantánea del progreso actual en el campo a través de discusiones extensas y brindan a los investigadores la oportunidad de caracterizan sus métodos de registro de imágenes en conjuntos de datos estandarizados recientemente publicados sobre resección de tumores cerebrales guiada por iUS. Nota de contenido: 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019) -- Comparison of active learning strategies applied to lung nodule segmentation in CT scans -- Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation -- XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis -- Data Augmentation based on Substituting Regional MRI Volume Scores -- Weakly supervised segmentation from extreme points -- Exploring the Relationship between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks -- DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs -- The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018 -- First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019) -- Hardware Acceleration of Persistent Homology Computation -- Deep Compressed Pneumonia Detection for Low-Power Embedded Devices -- D3MC: A Reinforcement Learning based Data-driven Dyna Model Compression -- An Analytical Method of Automatic Alignment for Electron Tomography -- Fixed-Point U-Net Quantization for Medical Image Segmentation -- Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019) -- Registration of ultrasound volumes based on Euclidean distance transform -- Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set -- Comparing deep learning strategies and attention mechanisms of discrete registration for multimodal image-guided interventions. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications inmedical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention : International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings [documento electrónico] / Zhou, Luping, ; Heller, Nicholas, ; Shi, Yiyu, ; Xiao, Yiming, ; Sznitman, Raphael, ; Cheplygina, Veronika, ; Mateus, Diana, ; Trucco, Emanuele, ; Hu, X. Sharon, ; Chen, Danny, ; Chabanas, Matthieu, ; Rivaz, Hassan, ; Reinertsen, Ingerid, . - 1 ed. . - [s.l.] : Springer, 2019 . - XX, 154 p. 62 ilustraciones, 48 ilustraciones en color.
ISBN : 978-3-030-33642-4
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 Inteligencia artificial Informática Médica Informática de la Salud Clasificación: 006.37 Resumen: Este libro constituye las actas conjuntas arbitradas del Cuarto Taller Internacional sobre Anotación a Gran Escala de Datos Biomédicos y SÃntesis de Etiquetas Expertas, LABELS 2019, el Primer Taller Internacional sobre Aprendizaje Consciente de Hardware para Imágenes Médicas e Intervención Asistida por Computadora, HAL-MICCAI 2019, y el Segundo Taller Internacional sobre Corrección del Cambio Cerebral con Ultrasonido Intraoperatorio, CuRIOUS 2019, celebrado junto con la 22.ª Conferencia Internacional sobre Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 8 artÃculos presentados En LABELS 2019, los 5 artÃculos presentados en HAL-MICCAI 2019 y los 3 artÃculos presentados en CuRIOUS 2019 fueron cuidadosamente revisados ​​y seleccionados entre numerosas presentaciones. Los artÃculos de LABELS presentan una variedad de enfoques para abordar un número limitado de etiquetas, desde el aprendizaje semisupervisado hasta el crowdsourcing. Los artÃculos de HAL-MICCAI cubren un amplio conjunto de aplicaciones de hardware en problemas médicos, incluida la segmentación de imágenes médicas, la tomografÃa electrónica, la detección de neumonÃa, etc. Los artÃculos de CuRIOUS brindan una instantánea del progreso actual en el campo a través de discusiones extensas y brindan a los investigadores la oportunidad de caracterizan sus métodos de registro de imágenes en conjuntos de datos estandarizados recientemente publicados sobre resección de tumores cerebrales guiada por iUS. Nota de contenido: 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS 2019) -- Comparison of active learning strategies applied to lung nodule segmentation in CT scans -- Robust Registration of Statistical Shape Models for Unsupervised Pathology Annotation -- XiangyaDerm: A Clinical Image Dataset of Asian Race for Skin Disease Aided Diagnosis -- Data Augmentation based on Substituting Regional MRI Volume Scores -- Weakly supervised segmentation from extreme points -- Exploring the Relationship between Segmentation Uncertainty, Segmentation Performance and Inter-observer Variability with Probabilistic Networks -- DeepIGeoS-V2: Deep Interactive Segmentation of Multiple Organs from Head and Neck Images with Lightweight CNNs -- The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018 -- First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention (HAL-MICCAI 2019) -- Hardware Acceleration of Persistent Homology Computation -- Deep Compressed Pneumonia Detection for Low-Power Embedded Devices -- D3MC: A Reinforcement Learning based Data-driven Dyna Model Compression -- An Analytical Method of Automatic Alignment for Electron Tomography -- Fixed-Point U-Net Quantization for Medical Image Segmentation -- Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2019) -- Registration of ultrasound volumes based on Euclidean distance transform -- Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set -- Comparing deep learning strategies and attention mechanisms of discrete registration for multimodal image-guided interventions. Tipo de medio : Computadora Summary : This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications inmedical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging : Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings Tipo de documento: documento electrónico Autores: Zhou, Luping, ; Sarikaya, Duygu, ; Kia, Seyed Mostafa, ; Speidel, Stefanie, ; Malpani, Anand, ; Hashimoto, Daniel, ; Habes, Mohamad, ; L¶fstedt, Tommy, ; Ritter, Kerstin, ; Wang, Hongzhi, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XVI, 114 p. 35 ilustraciones, 33 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-32695-1 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 Inteligencia artificial Clasificación: 006.37 Resumen: El CapÃtulo 5 está disponible en acceso abierto bajo una licencia internacional Creative Commons Attribution 4.0 a través de Springerlink. Nota de contenido: Proceedings of the Second International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0 2019) -- Feature Aggregation Decoder for Segmenting Laparoscopic Scenes -- Preoperative Planning for Guidewires employing Shape-Regularized Segmentation and Optimized Trajectories -- Guided unsupervised desmoking of laparoscopic images using Cycle-Desmoke -- Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration -- Live monitoring of hemodynamic changes with multispectral image analysis -- Towards a Cyber-Physical Systems Based Operating Room of the Future -- Proceedings of the Second International Workshop on Machine Learning in Clinical Neuroimaging: Entering the era of big data via transfer learning and data harmonization (MLCN 2019) -- Deep Transfer Learning For Whole-Brain FMRI Analyses -- Knowledge distillation for semi-supervised domain adaptation -- Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors -- Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation -- A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study -- Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites. Tipo de medio : Computadora Summary : Chapter 5 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging : Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings [documento electrónico] / Zhou, Luping, ; Sarikaya, Duygu, ; Kia, Seyed Mostafa, ; Speidel, Stefanie, ; Malpani, Anand, ; Hashimoto, Daniel, ; Habes, Mohamad, ; L¶fstedt, Tommy, ; Ritter, Kerstin, ; Wang, Hongzhi, . - 1 ed. . - [s.l.] : Springer, 2019 . - XVI, 114 p. 35 ilustraciones, 33 ilustraciones en color.
ISBN : 978-3-030-32695-1
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 Inteligencia artificial Clasificación: 006.37 Resumen: El CapÃtulo 5 está disponible en acceso abierto bajo una licencia internacional Creative Commons Attribution 4.0 a través de Springerlink. Nota de contenido: Proceedings of the Second International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0 2019) -- Feature Aggregation Decoder for Segmenting Laparoscopic Scenes -- Preoperative Planning for Guidewires employing Shape-Regularized Segmentation and Optimized Trajectories -- Guided unsupervised desmoking of laparoscopic images using Cycle-Desmoke -- Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration -- Live monitoring of hemodynamic changes with multispectral image analysis -- Towards a Cyber-Physical Systems Based Operating Room of the Future -- Proceedings of the Second International Workshop on Machine Learning in Clinical Neuroimaging: Entering the era of big data via transfer learning and data harmonization (MLCN 2019) -- Deep Transfer Learning For Whole-Brain FMRI Analyses -- Knowledge distillation for semi-supervised domain adaptation -- Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors -- Data Pooling and Sampling of Heterogeneous Image Data for White Matter Hyperintensity Segmentation -- A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study -- Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites. Tipo de medio : Computadora Summary : Chapter 5 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
