Título :	23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI
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, 819 p. 33 ilustraciones, 1 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-59725-2
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:	Angiography and Vessel Analysis -- Lightweight Double Attention-fused Networks for Intraoperative Stent Segmentation -- TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling -- Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction -- Branch-aware Double DQN for Centerline Extraction in Coronary CT Angiography -- Automatic CAD-RADS Scoring from CCTA Scans using Deep Learning -- Higher-Order Flux with Spherical Harmonics Transform for Vascular Analysis -- Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network -- Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement -- Time matters: Handling spatio-temporal perfusion information for automated TICI scoring -- ID-Fit: Intra-saccular Device adjustment for personalized cerebral aneurysm treatment -- JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation -- Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images -- Vascular surface segmentation for intracranial aneurysm isolation and quantification -- Breast Imaging -- Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities -- 2D X-ray mammography and 3D breast MRI registration -- A Second-order Subregion Pooling Network for Breast Ultrasound Lesion Segmentation -- Multi-Scale Gradational-Order Fusion Framework for Breast lesions Classification Using Ultrasound images -- Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network -- Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound -- MommiNet: Mammographic Multi-View Mass Identification Networks -- Multi-Site Evaluation of a Study-Level Classifier for Mammography using Deep Learning -- The case of missed cancers: Applying AI as a radiologist's safety net -- Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models -- Multi-task learning for detection and classification of cancer in screening mammography -- Colonoscopy -- Adaptive Context Selection for Polyp Segmentation -- PraNet: Parallel Reverse Attention Network for Polyp Segmentation -- Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy -- PolypSeg: an Efficient Context-aware Network for Polyp Segmentation from Colonoscopy Videos -- Endoscopic polyp segmentation using a hybrid 2D/3D CNN -- Dermatology -- A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images -- Fairness of Classifiers Across Skin Tones in Dermatology -- Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification -- Clinical-Inspired Network for Skin Lesion Recognition -- Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images -- Fetal Imaging -- Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets -- Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect -- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency -- Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy -- Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning -- Joint Image Quality Assessment and Brain Extraction of Fetal MRI using Deep Learning -- Heart and Lung Imaging -- Accelerated 4D Respiratory Motion-resolved Cardiac MRI with a Model-based Variational Network -- Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation -- ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation -- A Bottom-up Approach for Real-time Mitral Valve Annulus Modeling on 3D Echo Images -- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking andSegmentation in 4D Echocardiography -- Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets -- Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction -- Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study -- Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules -- Multi-stream Progressive Up-sampling Network for Dense CT Image Reconstruction -- Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Autoencoders -- Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification -- CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans -- Interpretable Identification of Interstitial Lung Diseases (ILD) Associated Findings from CT -- Learning with Sure Data for Nodule-Level Lung Cancer Prediction -- Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation -- Class-Aware Multi-Window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features -- Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation -- Deep Active Learning for Effective Pulmonary Nodule Detection -- Musculoskeletal Imaging -- Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity -- Improve bone age assessment by learning from anatomical local regions -- An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT -- Segmentation of Paraspinal Muscles at Varied Lumbar Spinal Levels by Explicit Saliency-Aware Learning -- Manifold Ordinal-Mixup for Ordered Classes inTW3-based Bone Age Assessment -- Contour-based Bone Axis Detection for X-Ray Guided Surgery on the Knee -- Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks -- Discriminative dictionary-embedded network for comprehensivevertebrae tumor diagnosis -- Multi-vertebrae segmentation from arbitrary spine MR images under global view -- A Convolutional Approach to Vertebrae Identification in Whole Spine MRI -- Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification -- Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection -- 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT -- SIMBA: Specific Identity Markers for Bone Age Assessment -- Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs -- Inferring the 3D Standing Spine Posture from 2D Radiographs -- Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters -- GAN-based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation -- Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition.
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 VI [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, 819 p. 33 ilustraciones, 1 ilustraciones en color.
ISBN : 978-3-030-59725-2
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:	Angiography and Vessel Analysis -- Lightweight Double Attention-fused Networks for Intraoperative Stent Segmentation -- TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling -- Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction -- Branch-aware Double DQN for Centerline Extraction in Coronary CT Angiography -- Automatic CAD-RADS Scoring from CCTA Scans using Deep Learning -- Higher-Order Flux with Spherical Harmonics Transform for Vascular Analysis -- Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network -- Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement -- Time matters: Handling spatio-temporal perfusion information for automated TICI scoring -- ID-Fit: Intra-saccular Device adjustment for personalized cerebral aneurysm treatment -- JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation -- Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images -- Vascular surface segmentation for intracranial aneurysm isolation and quantification -- Breast Imaging -- Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities -- 2D X-ray mammography and 3D breast MRI registration -- A Second-order Subregion Pooling Network for Breast Ultrasound Lesion Segmentation -- Multi-Scale Gradational-Order Fusion Framework for Breast lesions Classification Using Ultrasound images -- Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network -- Auto-weighting for Breast Cancer Classification in Multimodal Ultrasound -- MommiNet: Mammographic Multi-View Mass Identification Networks -- Multi-Site Evaluation of a Study-Level Classifier for Mammography using Deep Learning -- The case of missed cancers: Applying AI as a radiologist's safety net -- Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models -- Multi-task learning for detection and classification of cancer in screening mammography -- Colonoscopy -- Adaptive Context Selection for Polyp Segmentation -- PraNet: Parallel Reverse Attention Network for Polyp Segmentation -- Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy -- PolypSeg: an Efficient Context-aware Network for Polyp Segmentation from Colonoscopy Videos -- Endoscopic polyp segmentation using a hybrid 2D/3D CNN -- Dermatology -- A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images -- Fairness of Classifiers Across Skin Tones in Dermatology -- Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification -- Clinical-Inspired Network for Skin Lesion Recognition -- Multi-class Skin Lesion Segmentation for Cutaneous T-cell Lymphomas on High-Resolution Clinical Images -- Fetal Imaging -- Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets -- Data-Driven Multi-Contrast Spectral Microstructure Imaging with InSpect -- Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency -- Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy -- Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning -- Joint Image Quality Assessment and Brain Extraction of Fetal MRI using Deep Learning -- Heart and Lung Imaging -- Accelerated 4D Respiratory Motion-resolved Cardiac MRI with a Model-based Variational Network -- Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation -- ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation -- A Bottom-up Approach for Real-time Mitral Valve Annulus Modeling on 3D Echo Images -- A Semi-supervised Joint Network for Simultaneous Left Ventricular Motion Tracking andSegmentation in 4D Echocardiography -- Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets -- Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction -- Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study -- Learning Tumor Growth via Follow-Up Volume Prediction for Lung Nodules -- Multi-stream Progressive Up-sampling Network for Dense CT Image Reconstruction -- Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Autoencoders -- Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification -- CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans -- Interpretable Identification of Interstitial Lung Diseases (ILD) Associated Findings from CT -- Learning with Sure Data for Nodule-Level Lung Cancer Prediction -- Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation -- Class-Aware Multi-Window Adversarial Lung Nodule Synthesis Conditioned on Semantic Features -- Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation -- Deep Active Learning for Effective Pulmonary Nodule Detection -- Musculoskeletal Imaging -- Towards Robust Bone Age Assessment: Rethinking Label Noise and Ambiguity -- Improve bone age assessment by learning from anatomical local regions -- An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT -- Segmentation of Paraspinal Muscles at Varied Lumbar Spinal Levels by Explicit Saliency-Aware Learning -- Manifold Ordinal-Mixup for Ordered Classes inTW3-based Bone Age Assessment -- Contour-based Bone Axis Detection for X-Ray Guided Surgery on the Knee -- Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks -- Discriminative dictionary-embedded network for comprehensivevertebrae tumor diagnosis -- Multi-vertebrae segmentation from arbitrary spine MR images under global view -- A Convolutional Approach to Vertebrae Identification in Whole Spine MRI -- Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification -- Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection -- 3D Convolutional Sequence to Sequence Model for Vertebral Compression Fractures Identification in CT -- SIMBA: Specific Identity Markers for Bone Age Assessment -- Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs -- Inferring the 3D Standing Spine Posture from 2D Radiographs -- Generative Modelling of 3D in-silico Spongiosa with Controllable Micro-Structural Parameters -- GAN-based Realistic Bone Ultrasound Image and Label Synthesis for Improved Segmentation -- Robust Bone Shadow Segmentation from 2D Ultrasound Through Task Decomposition.
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