| TÃtulo : |
23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I |
| 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, 849 p. 257 ilustraciones |
| ISBN/ISSN/DL: |
978-3-030-59710-8 |
| 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 Bioinformática Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado |
| Ãndice 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 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas 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 vÃdeo 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; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. |
| Nota de contenido: |
Machine Learning Methodologies -- Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation -- Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency -- Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides -- Deep Reinforcement Active Learning for Medical Image Classification -- An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition -- Synthetic Sample Selection via Reinforcement Learning -- Dual-level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT -- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture -- Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net -- Have you forgotten? A method to assess ifmachine learning models have forgotten data -- Learning and Exploiting Interclass Visual Correlations for Medical Image Classification -- Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation -- Deep kNN for Medical Image Classification -- Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration -- DECAPS: Detail-oriented Capsule Networks -- Federated Simulation for Medical Imaging -- Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy -- Learning to Segment When Experts Disagree -- Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search -- Learning joint shape and appearance representations with metamorphic auto-encoders -- Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-related Esophageal Fistula Prediction from CT -- Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation -- Learning Rich Attention for Pediatric Bone Age Assessment -- Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction -- High-order Attention Networks for Medical Image Segmentation -- NAS-SCAM: Neural Architecture Search-based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification -- Scientific Discovery by Generating Counterfactuals using Image Translation -- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction -- Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses -- Interpretability-guided Content-based Medical Image Retrieval -- Domain aware medical image classifier interpretation by counterfactual impact analysis -- Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability -- Meta Corrupted Pixels Mining for Medical Image Segmentation -- UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation -- Difficulty-aware Meta-learning for Rare Disease Diagnosis -- Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification -- Automatic Data Augmentation for 3D Medical Image Segmentation -- MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation -- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations -- Dual-task Self-supervision for Cross-Modality Domain Adaptation -- Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation -- Test-time Unsupervised Domain Adaptation -- Self domain adapted network -- Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI -- User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation -- SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays -- Scribble-based Domain Adaptation via Deep Co-Segmentation -- Source-Relaxed Domain Adaptation for Image Segmentation -- Region-of-interest guided Supervoxel Inpainting for Self-supervision -- Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation -- Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation -- DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images -- Double-uncertainty Weighted Method for Semi-supervised Learning -- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images -- Local and Global Structure-aware Entropy Regularized Mean Teacher Model for 3D Left Atrium segmentation -- Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization -- Knowledge-guided Pretext Learning for Utero-placental Interface Detection -- Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy -- Semi-supervised Medical Image Classification with Global Latent Mixing -- Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation -- Semi-Supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is not Mean -- Predicting Potential Propensity of Adolescents to Drugs via New Semi-Supervised Deep Ordinal Regression Model -- Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet -- Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation -- Realistic Adversarial Data Augmentation for MR Image Segmentation -- Learning to Segment Anatomical Structures Accurately from One Exemplar -- Uncertainty estimates as data selection criteria to boost omni-supervised learning -- Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts -- Spatio-temporal Consistency and Negative LabelTransfer for 3D freehand US Segmentation -- Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation -- Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning -- Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling -- Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks -- Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation -- Knowledge distillation from multi-modal to mono-modal segmentation networks -- Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation -- Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty -- Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-Training with Very Sparse Annotation -- Probabilistic 3D surface reconstruction from sparse MRI information -- Can you trust predictive uncertainty under real dataset shifts in digital pathology? -- Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions. |
| En lÃnea: |
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 I [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, 849 p. 257 ilustraciones. ISBN : 978-3-030-59710-8 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 Bioinformática Sistemas de reconocimiento de patrones Aplicación informática en ciencias sociales y del comportamiento Computadoras y Educación BiologÃa Computacional y de Sistemas Reconocimiento de patrones automatizado |
| Ãndice 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 artÃculos completos revisados ​​presentados fueron cuidadosamente revisados ​​y seleccionados entre 1809 presentaciones en un proceso de revisión doble ciego. Los artÃculos están organizados en las siguientes secciones temáticas: Parte I: metodologÃas 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 vÃdeo 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; oftalmologÃa Parte VI: angiografÃa y análisis de vasos; imágenes de mama; colonoscopia; dermatologÃa; imágenes fetales; imágenes del corazón y los pulmones; imágenes musculoesqueléticas Parte VI: desarrollo cerebral y atlas; DWI y tractografÃa; redes cerebrales funcionales; neuroimagen; TomografÃa de emisión de positrones. |
| Nota de contenido: |
Machine Learning Methodologies -- Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation -- Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency -- Are fast labeling methods reliable? A case study of computer-aided expert annotations on microscopy slides -- Deep Reinforcement Active Learning for Medical Image Classification -- An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition -- Synthetic Sample Selection via Reinforcement Learning -- Dual-level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT -- BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture -- Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net -- Have you forgotten? A method to assess ifmachine learning models have forgotten data -- Learning and Exploiting Interclass Visual Correlations for Medical Image Classification -- Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation -- Deep kNN for Medical Image Classification -- Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration -- DECAPS: Detail-oriented Capsule Networks -- Federated Simulation for Medical Imaging -- Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy -- Learning to Segment When Experts Disagree -- Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search -- Learning joint shape and appearance representations with metamorphic auto-encoders -- Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-related Esophageal Fistula Prediction from CT -- Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation -- Learning Rich Attention for Pediatric Bone Age Assessment -- Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction -- High-order Attention Networks for Medical Image Segmentation -- NAS-SCAM: Neural Architecture Search-based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification -- Scientific Discovery by Generating Counterfactuals using Image Translation -- Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction -- Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses -- Interpretability-guided Content-based Medical Image Retrieval -- Domain aware medical image classifier interpretation by counterfactual impact analysis -- Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability -- Meta Corrupted Pixels Mining for Medical Image Segmentation -- UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation -- Difficulty-aware Meta-learning for Rare Disease Diagnosis -- Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification -- Automatic Data Augmentation for 3D Medical Image Segmentation -- MS-NAS: Multi-Scale Neural Architecture Search for Medical Image Segmentation -- Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations -- Dual-task Self-supervision for Cross-Modality Domain Adaptation -- Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation -- Test-time Unsupervised Domain Adaptation -- Self domain adapted network -- Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI -- User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation -- SALAD: Self-Supervised Aggregation Learning for Anomaly Detection on X-Rays -- Scribble-based Domain Adaptation via Deep Co-Segmentation -- Source-Relaxed Domain Adaptation for Image Segmentation -- Region-of-interest guided Supervoxel Inpainting for Self-supervision -- Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation -- Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation -- DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images -- Double-uncertainty Weighted Method for Semi-supervised Learning -- Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images -- Local and Global Structure-aware Entropy Regularized Mean Teacher Model for 3D Left Atrium segmentation -- Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization -- Knowledge-guided Pretext Learning for Utero-placental Interface Detection -- Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy -- Semi-supervised Medical Image Classification with Global Latent Mixing -- Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical Image Segmentation -- Semi-Supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is not Mean -- Predicting Potential Propensity of Adolescents to Drugs via New Semi-Supervised Deep Ordinal Regression Model -- Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet -- Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation -- Realistic Adversarial Data Augmentation for MR Image Segmentation -- Learning to Segment Anatomical Structures Accurately from One Exemplar -- Uncertainty estimates as data selection criteria to boost omni-supervised learning -- Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts -- Spatio-temporal Consistency and Negative LabelTransfer for 3D freehand US Segmentation -- Characterizing Label Errors: Confident Learning for Noisy-labeled Image Segmentation -- Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning -- Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modeling -- Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks -- Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation -- Knowledge distillation from multi-modal to mono-modal segmentation networks -- Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation -- Efficient Shapley Explanation For Features Importance Estimation Under Uncertainty -- Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-Training with Very Sparse Annotation -- Probabilistic 3D surface reconstruction from sparse MRI information -- Can you trust predictive uncertainty under real dataset shifts in digital pathology? -- Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions. |
| En lÃnea: |
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 I
