Título :	23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II
Tipo de documento:	documento electrónico
Autores:	Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo,
Mención de edición:	1 ed.
Editorial:	[s.l.] : Springer
Fecha de publicación:	2020
Número de páginas:	XXXVII, 785 p. 258 ilustraciones, 228 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-59713-9
Nota general:	Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave:	Visión por computador  Inteligencia artificial  Ciencias sociales  Sistemas de reconocimiento de patrones  Bioinformática  Aplicación informática en ciencias sociales y del comportamiento  Computadoras y Educación  Reconocimiento de patrones automatizado  Biología Computacional y de Sistemas
Índice Dewey:	006.37 Visión artificial
Resumen:	El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artículos están organizados en las siguientes secciones temáticas: Parte I: metodologías de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vídeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmología Parte VI: angiografía y análisis de vasos; imágenes de mama; colonoscopia; dermatología; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografía; redes cerebrales funcionales; neuroimagen; Tomografía de emisión de positrones.
Nota de contenido:	Image Reconstruction -- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images -- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations -- Active MR k-space Sampling with Reinforcement Learning -- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts -- Joint reconstruction and bias field correction for undersampled MR imaging -- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping -- End-to-End Variational Networks for Accelerated MRI Reconstruction -- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning -- MRI Image Reconstruction via Learning Optimization Using Neural ODEs -- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms -- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN -- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions -- Learned Proximal Networks for Quantitative Susceptibility Mapping -- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction -- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography -- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network -- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness -- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning -- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image -- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning -- Prediction and Diagnosis -- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response -- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients -- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography -- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data -- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues -- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution -- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging -- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions -- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer -- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI -- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved ComputerAided Diagnosis -- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN -- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks -- Cross-Domain Methods and Reconstruction -- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation -- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings -- Unlearning Scanner Bias for MRI Harmonisation -- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model -- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy -- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph -- Domain Adaptation for Ultrasound Beamforming -- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction -- Domain Adaptation -- Improve Unseen Domain Generalization via Enhanced Local Color Transformation and Augmentation -- Transport-based Joint Distribution Alignment for Multi-site Autism Spectrum Disorder Diagnosis using Resting-state fMRI -- Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs -- Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening -- Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains -- Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes -- Unsupervised Graph Domain Adaptation for Neurodevelopmental Disorders Diagnosis -- JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering -- MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint -- Machine Learning Applications -- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment -- Domain-specific loss design for unsupervised physical training: A new approach to modeling medical MLsolutions -- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect -- Chest X-ray Report Generation through Fine-Grained Label Learning -- Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time -- A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction -- Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications -- Large-scale inference of liver fat with neural networks on UK Biobank body MRI -- BUNET: Blind Medical Image Segmentation Based on Secure UNET -- Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape -- Decision Support for Intoxication Prediction Using Graph Convolutional Networks -- Latent-Graph Learning for Disease Prediction -- Generative Adversarial Networks -- BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification -- Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement -- Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN -- GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI -- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network -- AGAN: An Anatomy Corrector Conditional Generative Adversarial Network -- SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI -- Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation -- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields -- Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation -- Graded Image Generation Using Stratified CycleGAN -- Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i

23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II [documento electrónico] / Martel, Anne L., ; Abolmaesumi, Purang, ; Stoyanov, Danail, ; Mateus, Diana, ; Zuluaga, Maria A., ; Zhou, S. Kevin, ; Racoceanu, Daniel, ; Joskowicz, Leo,  . -  1 ed.  . - [s.l.] : Springer, 2020 . - XXXVII, 785 p. 258 ilustraciones, 228 ilustraciones en color.
ISBN : 978-3-030-59713-9
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.

Palabras clave:	Visión por computador  Inteligencia artificial  Ciencias sociales  Sistemas de reconocimiento de patrones  Bioinformática  Aplicación informática en ciencias sociales y del comportamiento  Computadoras y Educación  Reconocimiento de patrones automatizado  Biología Computacional y de Sistemas
Índice Dewey:	006.37 Visión artificial
Resumen:	El conjunto de siete volúmenes LNCS 12261, 12262, 12263, 12264, 12265, 12266 y 12267 constituye las actas arbitradas de la 23.ª Conferencia Internacional sobre Computación de Imágenes Médicas e Intervención Asistida por Computadora, MICCAI 2020, celebrada en Lima, Perú, en octubre. 2020. La conferencia se realizó de manera virtual debido a la pandemia de COVID-19. Los 542 artículos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artículos están organizados en las siguientes secciones temáticas: Parte I: metodologías de aprendizaje automático Parte II: reconstrucción de imágenes; predicción y diagnóstico; métodos y reconstrucción entre dominios; adaptación de dominio; aplicaciones de aprendizaje automático; redes generativas adversarias Parte III: aplicaciones CAI; registro de imagen; instrumentación y detección de fase quirúrgica; navegación y visualización; imágenes por ultrasonido; análisis de imágenes de vídeo Parte IV: segmentación; modelos de formas y detección de puntos de referencia Parte V: imágenes biológicas, ópticas y microscópicas; segmentación celular y normalización de tinciones; análisis de imágenes histopatológicas; oftalmología Parte VI: angiografía y análisis de vasos; imágenes de mama; colonoscopia; dermatología; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografía; redes cerebrales funcionales; neuroimagen; Tomografía de emisión de positrones.
Nota de contenido:	Image Reconstruction -- Improving Amide Proton Transfer-weighted MRI Reconstruction using T2-weighted Images -- Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations -- Active MR k-space Sampling with Reinforcement Learning -- Fast Correction of Eddy-Current and Susceptibility-Induced Distortions Using Rotation-Invariant Contrasts -- Joint reconstruction and bias field correction for undersampled MR imaging -- Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping -- End-to-End Variational Networks for Accelerated MRI Reconstruction -- 3d-SMRnet: Achieving a new quality of MPI system matrix recovery by deep learning -- MRI Image Reconstruction via Learning Optimization Using Neural ODEs -- An evolutionary framework for microstructure-sensitive generalized diffusion gradient waveforms -- Lesion Mask-based Simultaneous Synthesis of Anatomic and Molecular MR Images using a GAN -- T2 Mapping from Super-Resolution-Reconstructed Clinical Fast Spin Echo Magnetic Resonance Acquisitions -- Learned Proximal Networks for Quantitative Susceptibility Mapping -- Learning A Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction -- Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography -- Acceleration of High-resolution 3D MR Fingerprinting via a Graph Convolutional Network -- Deep Attentive Wasserstein Generative Adversarial Network for MRI Reconstruction with Recurrent Context-Awareness -- Learning MRI $k$-Space Subsampling Pattern using Progressive Weight Pruning -- Model-driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image -- Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning -- Prediction and Diagnosis -- MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response -- M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients -- Automatic Detection of Free Intra-Abdominal Air in Computed Tomography -- Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Deep Learning with Integrative Imaging, Molecular and Demographic Data -- Geodesically Smoothed Tensor Features for Pulmonary Hypertension Prognosis using the Heart and Surrounding Tissues -- Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution -- DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Dynamic Contrast-Enhanced CT Imaging -- Holistic Analysis of Abdominal CT for Predicting the Grade of Dysplasia of Pancreatic Lesions -- Feature-enhanced Graph Networks for Genetic Mutational Prediction Using Histopathological Images in Colon cancer -- Spatial-And-Context aware (SpACe) "virtual biopsy'' radiogenomic maps to target tumor mutational status on structural MRI -- CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved ComputerAided Diagnosis -- Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN -- Learning Differential Diagnosis of Skin Conditions with Co-occurrence Supervision using Graph Convolutional Networks -- Cross-Domain Methods and Reconstruction -- Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation -- Dynamic memory to alleviate catastrophic forgetting in continuous learning settings -- Unlearning Scanner Bias for MRI Harmonisation -- Cross-Domain Image Translation by Shared Latent Gaussian Mixture Model -- Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy -- X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph -- Domain Adaptation for Ultrasound Beamforming -- CDF-Net: Cross-Domain Fusion Network for accelerated MRI reconstruction -- Domain Adaptation -- Improve Unseen Domain Generalization via Enhanced Local Color Transformation and Augmentation -- Transport-based Joint Distribution Alignment for Multi-site Autism Spectrum Disorder Diagnosis using Resting-state fMRI -- Automatic and interpretable model for periodontitis diagnosis in panoramic radiographs -- Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening -- Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains -- Automatic Plane Adjustment of Orthopedic Intraoperative Flat Panel Detector CT-Volumes -- Unsupervised Graph Domain Adaptation for Neurodevelopmental Disorders Diagnosis -- JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering -- MI^2GAN: Generative Adversarial Network for Medical Image Domain Adaptation using Mutual Information Constraint -- Machine Learning Applications -- Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment -- Domain-specific loss design for unsupervised physical training: A new approach to modeling medical MLsolutions -- Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect -- Chest X-ray Report Generation through Fine-Grained Label Learning -- Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time -- A Deep Bayesian Video Analysis Framework: Towards a More Robust Estimation of Ejection Fraction -- Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications -- Large-scale inference of liver fat with neural networks on UK Biobank body MRI -- BUNET: Blind Medical Image Segmentation Based on Secure UNET -- Temporal-consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape -- Decision Support for Intoxication Prediction Using Graph Convolutional Networks -- Latent-Graph Learning for Disease Prediction -- Generative Adversarial Networks -- BR-GAN: Bilateral Residual Generating Adversarial Network for Mammogram Classification -- Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement -- Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN -- GANDALF: Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI -- Brain MR to PET Synthesis via Bidirectional Generative Adversarial Network -- AGAN: An Anatomy Corrector Conditional Generative Adversarial Network -- SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI -- Flow-based Deformation Guidance for Unpaired Multi-Contrast MRI Image-to-Image Translation -- Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields -- Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation -- Graded Image Generation Using Stratified CycleGAN -- Prediction of Plantar Shear Stress Distribution by Conditional GAN with Attention Mechanism.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i