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TÃtulo : Machine Learning in Medical Imaging : 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings Tipo de documento: documento electrónico Autores: Suk, Heung-Il, ; Liu, Mingxia, ; Yan, Pingkun, ; Lian, Chunfeng, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2019 Número de páginas: XVIII, 695 p. 310 ilustraciones, 245 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-32692-0 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 Inteligencia artificial Clasificación: 006.37 Resumen: Este libro constituye las actas del décimo Taller Internacional sobre Aprendizaje Automático en Imágenes Médicas, MLMI 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 78 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 158 presentaciones. Se centran en las principales tendencias y desafÃos en el área, con el objetivo de identificar nuevas técnicas de vanguardia y sus usos en imágenes médicas. Los temas tratados son: aprendizaje profundo, aprendizaje generativo adversario, aprendizaje conjunto, aprendizaje disperso, aprendizaje multitarea, aprendizaje multivista, aprendizaje múltiple y aprendizaje por refuerzo, con sus aplicaciones al análisis de imágenes médicas, detección y diagnóstico asistido por computadora, fusión multimodal, reconstrucción de imágenes, recuperación de imágenes, análisis de imágenes celulares, imágenes moleculares, patologÃa digital, etc. Nota de contenido: rain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization -- Spatial Regularized Classification Network for Spinal Dislocation Diagnosis -- Globally-Aware Multiple Instance Classifier for Breast Cancer Screening -- Advancing Pancreas Segmentation in Multi-protocol MRI Volumes using Hausdorff-Sine Loss Function -- WSI-Net: Branch-based and Hierarchy-aware Network for Segmentation and Classification of Breast Histopathological Whole-slide Images -- Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information -- MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network -- DCCL: A Benchmark for Cervical Cytology Analysis -- Smartphone-Supported Malaria Diagnosis Based on Deep Learning -- Children's Neuroblastoma Segmentation using Morphological Features -- GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images -- Deep Active Lesion Segmentation -- Infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning -- A Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification -- End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation -- Privacy-preserving Federated Brain Tumour Segmentation -- Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology Images -- Semi-Supervised Multi-Task Learning With Chest X-Ray Images -- Novel Bi-directional Images Synthesis based on WGAN-GP with GMM-based Noise Generation -- Pseudo-labeled bootstrapping and multi-stage transfer learning for the classification and localization of dysplasia in Barrett's Esophagus -- Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images -- Boundary Aware Networks for Medical Image Segmentation -- Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks -- Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration -- Joint Shape Representation and Classification for Detecting PDAC -- FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans -- Weakly supervised segmentation by a deep geodesic prior -- Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks -- Correspondence-Steered Volumetric Descriptor Learning Using Deep Functional Maps -- Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI -- Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation -- Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-Unet -- Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images -- Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation -- Detecting abnormalities in resting-state dynamics: An unsupervised learning approach -- Distanced LSTM: Time-Distanced Gates in Long Short-Term MemoryModels for Lung Cancer Detection -- Dense-residual Attention Network for Skin Lesion Segmentation -- Confounder-Aware Visualization of ConvNets -- Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks -- Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection -- Unsupervised Lesion Detection with Locally Gaussian Approximation -- A Hybrid Multi-atrous and Multi-scale Network for Liver Lesion Detection -- BOLD fMRI-based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks -- Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI -- Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI -- A Maximum Entropy Deep Reinforcement Learning Neural Tracker -- Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation -- Select, Attend, and Transfer: Light, Learnable Skip Connections -- Learning-based Bone Quality Classification Method for Spinal Metastasis -- Automated Segmentation of Skin Lesion Based on Pyramid Attention Network -- Relu cascade of feature pyramid networks for CT pulmonary nodule detection -- Joint Localization of Optic Disc and Fovea in Ultra-Widefield Fundus Images -- Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures -- Lesion Detection by Efficiently Bridging 3D Context -- Communal Domain Learning for Registration in Drifted Image Spaces -- Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping -- Semantic filtering through deep source separation on microscopy images -- Adaptive Functional Connectivity Network using Parallel Hierarchical BiLSTM for MCI Diagnosis -- Multi-Template based Auto-weighted Adaptive Structural Learning for ASD Diagnosis -- Learn to Step-wise Focus on Targets for Biomedical Image Segmentation -- Renal Cell Carcinoma Staging with Learnable Image Histogram-based Deep Neural Network -- Weakly Supervised Learning Strategy for Lung Defect Segmentation -- Gated Recurrent Neural Networks for Accelerated Ventilation MRI -- A Cascaded Multi-Modality Analysis in Mild Cognitive Impairment -- Deep Residual Learning for Instrument Segmentation in Robotic Surgery -- Deep learning model integrating dilated convolution and deep supervision for brain tumor segmentation in multi-parametric MRI -- A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs -- Automatic Fetal Brain Extraction Using Multi-Stage U-Net with Deep Supervision -- Cross-Modal Attention-Guided Convolutional Network for Multi-Modal Cardiac Segmentation -- High- and Low-Level Feature Enhancement for Medical Image Segmentation -- Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation -- An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis -- Tree-LSTM: Using LSTM to Encode Memory in Anatomical Tree Prediction from 3D Images -- FAIM-A ConvNet Method for Unsupervised 3D Medical Image Registration -- Functional data and long short-term memory networks for diagnosis of Parkinson's Disease -- Joint Holographic Detection and Reconstruction -- Reinforced Transformer for Medical Image Captioning -- Multi Task Convolutional Neural Network for Joint Bone Age Assessment and Ossification Center Detection from Hand Radiograph. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning in Medical Imaging : 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings [documento electrónico] / Suk, Heung-Il, ; Liu, Mingxia, ; Yan, Pingkun, ; Lian, Chunfeng, . - 1 ed. . - [s.l.] : Springer, 2019 . - XVIII, 695 p. 310 ilustraciones, 245 ilustraciones en color.
ISBN : 978-3-030-32692-0
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 Inteligencia artificial Clasificación: 006.37 Resumen: Este libro constituye las actas del décimo Taller Internacional sobre Aprendizaje Automático en Imágenes Médicas, MLMI 2019, celebrado junto con MICCAI 2019, en Shenzhen, China, en octubre de 2019. Los 78 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 158 presentaciones. Se centran en las principales tendencias y desafÃos en el área, con el objetivo de identificar nuevas técnicas de vanguardia y sus usos en imágenes médicas. Los temas tratados son: aprendizaje profundo, aprendizaje generativo adversario, aprendizaje conjunto, aprendizaje disperso, aprendizaje multitarea, aprendizaje multivista, aprendizaje múltiple y aprendizaje por refuerzo, con sus aplicaciones al análisis de imágenes médicas, detección y diagnóstico asistido por computadora, fusión multimodal, reconstrucción de imágenes, recuperación de imágenes, análisis de imágenes celulares, imágenes moleculares, patologÃa digital, etc. Nota de contenido: rain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization -- Spatial Regularized Classification Network for Spinal Dislocation Diagnosis -- Globally-Aware Multiple Instance Classifier for Breast Cancer Screening -- Advancing Pancreas Segmentation in Multi-protocol MRI Volumes using Hausdorff-Sine Loss Function -- WSI-Net: Branch-based and Hierarchy-aware Network for Segmentation and Classification of Breast Histopathological Whole-slide Images -- Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information -- MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network -- DCCL: A Benchmark for Cervical Cytology Analysis -- Smartphone-Supported Malaria Diagnosis Based on Deep Learning -- Children's Neuroblastoma Segmentation using Morphological Features -- GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images -- Deep Active Lesion Segmentation -- Infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning -- A Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification -- End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation -- Privacy-preserving Federated Brain Tumour Segmentation -- Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology Images -- Semi-Supervised Multi-Task Learning With Chest X-Ray Images -- Novel Bi-directional Images Synthesis based on WGAN-GP with GMM-based Noise Generation -- Pseudo-labeled bootstrapping and multi-stage transfer learning for the classification and localization of dysplasia in Barrett's Esophagus -- Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound Images -- Boundary Aware Networks for Medical Image Segmentation -- Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks -- Morphological Simplification of Brain MR Images by Deep Learning for Facilitating Deformable Registration -- Joint Shape Representation and Classification for Detecting PDAC -- FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans -- Weakly supervised segmentation by a deep geodesic prior -- Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks -- Correspondence-Steered Volumetric Descriptor Learning Using Deep Functional Maps -- Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI -- Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation -- Automatic Couinaud Segmentation from CT Volumes on Liver Using GLC-Unet -- Biomedical Image Segmentation by Retina-like Sequential Attention Mechanism Using Only A Few Training Images -- Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation -- Detecting abnormalities in resting-state dynamics: An unsupervised learning approach -- Distanced LSTM: Time-Distanced Gates in Long Short-Term MemoryModels for Lung Cancer Detection -- Dense-residual Attention Network for Skin Lesion Segmentation -- Confounder-Aware Visualization of ConvNets -- Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks -- Modelling Airway Geometry as Stock Market Data using Bayesian Changepoint Detection -- Unsupervised Lesion Detection with Locally Gaussian Approximation -- A Hybrid Multi-atrous and Multi-scale Network for Liver Lesion Detection -- BOLD fMRI-based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks -- Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI -- Unsupervised Conditional Consensus Adversarial Network for Brain Disease Identification with Structural MRI -- A Maximum Entropy Deep Reinforcement Learning Neural Tracker -- Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation -- Select, Attend, and Transfer: Light, Learnable Skip Connections -- Learning-based Bone Quality Classification Method for Spinal Metastasis -- Automated Segmentation of Skin Lesion Based on Pyramid Attention Network -- Relu cascade of feature pyramid networks for CT pulmonary nodule detection -- Joint Localization of Optic Disc and Fovea in Ultra-Widefield Fundus Images -- Multi-Scale Attentional Network for Multi-Focal Segmentation of Active Bleed after Pelvic Fractures -- Lesion Detection by Efficiently Bridging 3D Context -- Communal Domain Learning for Registration in Drifted Image Spaces -- Conv2Warp: An unsupervised deformable image registration with continuous convolution and warping -- Semantic filtering through deep source separation on microscopy images -- Adaptive Functional Connectivity Network using Parallel Hierarchical BiLSTM for MCI Diagnosis -- Multi-Template based Auto-weighted Adaptive Structural Learning for ASD Diagnosis -- Learn to Step-wise Focus on Targets for Biomedical Image Segmentation -- Renal Cell Carcinoma Staging with Learnable Image Histogram-based Deep Neural Network -- Weakly Supervised Learning Strategy for Lung Defect Segmentation -- Gated Recurrent Neural Networks for Accelerated Ventilation MRI -- A Cascaded Multi-Modality Analysis in Mild Cognitive Impairment -- Deep Residual Learning for Instrument Segmentation in Robotic Surgery -- Deep learning model integrating dilated convolution and deep supervision for brain tumor segmentation in multi-parametric MRI -- A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs -- Automatic Fetal Brain Extraction Using Multi-Stage U-Net with Deep Supervision -- Cross-Modal Attention-Guided Convolutional Network for Multi-Modal Cardiac Segmentation -- High- and Low-Level Feature Enhancement for Medical Image Segmentation -- Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation -- An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis -- Tree-LSTM: Using LSTM to Encode Memory in Anatomical Tree Prediction from 3D Images -- FAIM-A ConvNet Method for Unsupervised 3D Medical Image Registration -- Functional data and long short-term memory networks for diagnosis of Parkinson's Disease -- Joint Holographic Detection and Reconstruction -- Reinforced Transformer for Medical Image Captioning -- Multi Task Convolutional Neural Network for Joint Bone Age Assessment and Ossification Center Detection from Hand Radiograph. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 78 papers presented in this volume were carefully reviewed and selected from 158 submissions. They focus on major trends and challenges in the area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. . Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
TÃtulo : Machine Learning in Medical Imaging : 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings Tipo de documento: documento electrónico Autores: Liu, Mingxia, ; Yan, Pingkun, ; Lian, Chunfeng, ; Cao, Xiaohuan, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2020 Número de páginas: XV, 686 p. 327 ilustraciones, 230 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-59861-7 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 Sistemas de reconocimiento de patrones Software de la aplicacion Reconocimiento de patrones automatizado Aplicaciones informáticas y de sistemas de información Clasificación: 006.37 Resumen: Este libro constituye las actas del 11.º Taller Internacional sobre Aprendizaje Automático en Imágenes Médicas, MLMI 2020, celebrado en conjunto con MICCAI 2020, en Lima, Perú, en octubre de 2020. La conferencia se llevó a cabo virtualmente debido a la pandemia de COVID-19. Los 68 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 101 presentaciones. Se centran en las principales tendencias y desafÃos en el área mencionada anteriormente, con el objetivo de identificar nuevas técnicas de vanguardia y sus usos en imágenes médicas. Los temas tratados son: aprendizaje profundo, aprendizaje generativo adversario, aprendizaje conjunto, aprendizaje disperso, aprendizaje multitarea, aprendizaje multivista, aprendizaje múltiple y aprendizaje por refuerzo, con sus aplicaciones al análisis de imágenes médicas, detección y diagnóstico asistido por computadora, fusión multimodal, reconstrucción de imágenes, recuperación de imágenes, análisis de imágenes celulares, imágenes moleculares, patologÃa digital, etc. Nota de contenido: Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder with Resting-State fMRI -- Error Attention Interactive Segmentation of Medical Images through Matting and Fusion -- A Novel fMRI Representation Learning Framework with GAN -- Semi-supervised Segmentation with Self-Training Based on Quality Estimation and Refinement -- 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies -- Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network -- Self-Recursive Contextual Network for Unsupervised 3D Medical Image Registration -- Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy -- Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows -- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest -- A 3D+2D CNN Approach Incorporating BoundaryLoss for Stroke Lesion Segmentation -- Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network -- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior -- Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation -- GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes -- Anatomy-Aware Cardiac Motion Estimation -- Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation -- LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI -- Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation -- Boundary-aware Network for Kidney Tumor Segmentation -- O-Net: An Overall Convolutional Network for Segmentation Tasks -- Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints -- EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis -- Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation -- Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer -- Exploring Functional Difference between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks -- Detection of Ischemic Infarct Core in Non-Contrast Computed Tomography -- Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers -- Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients -- Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet -- Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification -- Multi-tasking Siamese Networks for Breast Mass Detection using Dual-view Mammogram Matching -- 3D Volume Reconstruction from Single Lateral X-ray Image via Cross-Modal Discrete Embedding Transition -- Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks -- A Deep Network for Joint Registration and Reconstruction of Images with Pathologies -- Learning Conditional Deformable Shape Templates for Brain Anatomy -- Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity -- Unsupervised Learning for Spherical Surface Registration -- Anatomy-guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI -- Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization -- Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors -- Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening -- Importance Driven Continual Learning for Segmentation Across Domains -- RDCNet: Instance segmentation with a minimalist recurrent residual network -- Automatic Segmentation of Achilles Tendon Tissues using Deep Convolutional Neural Network -- An End to End System for Measuring Axon Growth -- Interwound Structural and Functional Difference Between Preterm and Term Infant Brains Revealed by Multi-view CCA -- Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling -- Unsupervised Learning-based Nonrigid Registration of High Resolution Histology Images -- Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images -- Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation -- Gambling Adversarial Nets for Hard Sample Mining and Structured Prediction: Application in Ultrasound Thyroid Nodule Segmentation -- Mammographic Image Conversion between Source and Target Acquisition Systems using CGAN -- An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation -- Neural Architecture Search for Microscopy CellSegmentation -- Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection -- Predicting Catheter Ablation Outcomes from Heart Rhythm Time-series: Less Is More -- AdaBoosted Deep Ensembles: Getting Maximum Performance Out of Small Training Datasets -- Cross-Task Representation Learning for Anatomical Landmark Detection -- Cycle Ynet: Semi-supervised Tracking of 3D Anatomical Landmarks -- Learning Hierarchical Semantic Correspondence and Gland Instance Segmentation -- Open-Set Recognition for Skin Lesions using Dermoscopic Images -- End-to-End Coordinate Regression Model with Attention-Guided Mechanism for Landmark Localization in 3D Medical Images -- Enhanced MRI Reconstruction Network using Neural Architecture Search -- Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets -- Noise-aware Standard-dose PET Reconstruction Using General and Adaptive Robust Loss -- Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation -- Informative Feature-guided Siamese Network for Early Diagnosis of ASD. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning in Medical Imaging : 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings [documento electrónico] / Liu, Mingxia, ; Yan, Pingkun, ; Lian, Chunfeng, ; Cao, Xiaohuan, . - 1 ed. . - [s.l.] : Springer, 2020 . - XV, 686 p. 327 ilustraciones, 230 ilustraciones en color.
ISBN : 978-3-030-59861-7
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 Sistemas de reconocimiento de patrones Software de la aplicacion Reconocimiento de patrones automatizado Aplicaciones informáticas y de sistemas de información Clasificación: 006.37 Resumen: Este libro constituye las actas del 11.º Taller Internacional sobre Aprendizaje Automático en Imágenes Médicas, MLMI 2020, celebrado en conjunto con MICCAI 2020, en Lima, Perú, en octubre de 2020. La conferencia se llevó a cabo virtualmente debido a la pandemia de COVID-19. Los 68 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados entre 101 presentaciones. Se centran en las principales tendencias y desafÃos en el área mencionada anteriormente, con el objetivo de identificar nuevas técnicas de vanguardia y sus usos en imágenes médicas. Los temas tratados son: aprendizaje profundo, aprendizaje generativo adversario, aprendizaje conjunto, aprendizaje disperso, aprendizaje multitarea, aprendizaje multivista, aprendizaje múltiple y aprendizaje por refuerzo, con sus aplicaciones al análisis de imágenes médicas, detección y diagnóstico asistido por computadora, fusión multimodal, reconstrucción de imágenes, recuperación de imágenes, análisis de imágenes celulares, imágenes moleculares, patologÃa digital, etc. Nota de contenido: Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder with Resting-State fMRI -- Error Attention Interactive Segmentation of Medical Images through Matting and Fusion -- A Novel fMRI Representation Learning Framework with GAN -- Semi-supervised Segmentation with Self-Training Based on Quality Estimation and Refinement -- 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies -- Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network -- Self-Recursive Contextual Network for Unsupervised 3D Medical Image Registration -- Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy -- Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows -- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest -- A 3D+2D CNN Approach Incorporating BoundaryLoss for Stroke Lesion Segmentation -- Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network -- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior -- Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation -- GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes -- Anatomy-Aware Cardiac Motion Estimation -- Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation -- LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI -- Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation -- Boundary-aware Network for Kidney Tumor Segmentation -- O-Net: An Overall Convolutional Network for Segmentation Tasks -- Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints -- EczemaNet: Automating Detection and Severity Assessment of Atopic Dermatitis -- Deep Distance Map Regression Network with Shape-aware Loss for Imbalanced Medical Image Segmentation -- Joint Appearance-Feature Domain Adaptation: Application to QSM Segmentation Transfer -- Exploring Functional Difference between Gyri and Sulci via Region-Specific 1D Convolutional Neural Networks -- Detection of Ischemic Infarct Core in Non-Contrast Computed Tomography -- Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers -- Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients -- Structural Connectivity Enriched Functional Brain Network using Simplex Regression with GraphNet -- Constructing High-Order Dynamic Functional Connectivity Networks from Resting-State fMRI for Brain Dementia Identification -- Multi-tasking Siamese Networks for Breast Mass Detection using Dual-view Mammogram Matching -- 3D Volume Reconstruction from Single Lateral X-ray Image via Cross-Modal Discrete Embedding Transition -- Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks -- A Deep Network for Joint Registration and Reconstruction of Images with Pathologies -- Learning Conditional Deformable Shape Templates for Brain Anatomy -- Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity -- Unsupervised Learning for Spherical Surface Registration -- Anatomy-guided Convolutional Neural Network for Motion Correction in Fetal Brain MRI -- Gyral Growth Patterns of Macaque Brains Revealed by Scattered Orthogonal Nonnegative Matrix Factorization -- Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors -- Hierarchical and Robust Pathology Image Reading for High-Throughput Cervical Abnormality Screening -- Importance Driven Continual Learning for Segmentation Across Domains -- RDCNet: Instance segmentation with a minimalist recurrent residual network -- Automatic Segmentation of Achilles Tendon Tissues using Deep Convolutional Neural Network -- An End to End System for Measuring Axon Growth -- Interwound Structural and Functional Difference Between Preterm and Term Infant Brains Revealed by Multi-view CCA -- Graph Convolutional Network Based Point Cloud for Head and Neck Vessel Labeling -- Unsupervised Learning-based Nonrigid Registration of High Resolution Histology Images -- Additive Angular Margin for Few Shot Learning to Classify Clinical Endoscopy Images -- Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation -- Gambling Adversarial Nets for Hard Sample Mining and Structured Prediction: Application in Ultrasound Thyroid Nodule Segmentation -- Mammographic Image Conversion between Source and Target Acquisition Systems using CGAN -- An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation -- Neural Architecture Search for Microscopy CellSegmentation -- Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using Vascular Pattern Detection -- Predicting Catheter Ablation Outcomes from Heart Rhythm Time-series: Less Is More -- AdaBoosted Deep Ensembles: Getting Maximum Performance Out of Small Training Datasets -- Cross-Task Representation Learning for Anatomical Landmark Detection -- Cycle Ynet: Semi-supervised Tracking of 3D Anatomical Landmarks -- Learning Hierarchical Semantic Correspondence and Gland Instance Segmentation -- Open-Set Recognition for Skin Lesions using Dermoscopic Images -- End-to-End Coordinate Regression Model with Attention-Guided Mechanism for Landmark Localization in 3D Medical Images -- Enhanced MRI Reconstruction Network using Neural Architecture Search -- Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets -- Noise-aware Standard-dose PET Reconstruction Using General and Adaptive Robust Loss -- Semi-supervised Transfer Learning for Infant Cerebellum Tissue Segmentation -- Informative Feature-guided Siamese Network for Early Diagnosis of ASD. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning in Medical Imaging / Lian, Chunfeng ; Cao, Xiaohuan ; Rekik, Islem ; Xu, Xuanang ; Yan, Pingkun
TÃtulo : Machine Learning in Medical Imaging : 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings Tipo de documento: documento electrónico Autores: Lian, Chunfeng, ; Cao, Xiaohuan, ; Rekik, Islem, ; Xu, Xuanang, ; Yan, Pingkun, Mención de edición: 1 ed. Editorial: [s.l.] : Springer Fecha de publicación: 2021 Número de páginas: XVIII, 704 p. 248 ilustraciones, 232 ilustraciones en color. ISBN/ISSN/DL: 978-3-030-87589-3 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 IngenierÃa Informática Red de computadoras Sistemas de reconocimiento de patrones Bioinformática IngenierÃa Informática y Redes Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Clasificación: 006.37 Resumen: Este libro constituye las actas del 12.º Taller internacional sobre aprendizaje automático en imágenes médicas, MLMI 2021, celebrado junto con MICCAI 2021, en Estrasburgo, Francia, en septiembre de 2021.* Los 71 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados de 92 presentaciones. Se centran en las principales tendencias y desafÃos en el área mencionada anteriormente, con el objetivo de identificar nuevas técnicas de vanguardia y sus usos en imágenes médicas. Los temas tratados son: aprendizaje profundo, aprendizaje generativo adversario, aprendizaje conjunto, aprendizaje disperso, aprendizaje multitarea, aprendizaje multivista, aprendizaje múltiple y aprendizaje por refuerzo, con sus aplicaciones al análisis de imágenes médicas, detección y diagnóstico asistido por computadora, fusión multimodal, reconstrucción de imágenes, recuperación de imágenes, análisis de imágenes celulares, imágenes moleculares, patologÃa digital, etc. *El taller se realizó de manera virtual. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] Machine Learning in Medical Imaging : 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings [documento electrónico] / Lian, Chunfeng, ; Cao, Xiaohuan, ; Rekik, Islem, ; Xu, Xuanang, ; Yan, Pingkun, . - 1 ed. . - [s.l.] : Springer, 2021 . - XVIII, 704 p. 248 ilustraciones, 232 ilustraciones en color.
ISBN : 978-3-030-87589-3
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 IngenierÃa Informática Red de computadoras Sistemas de reconocimiento de patrones Bioinformática IngenierÃa Informática y Redes Reconocimiento de patrones automatizado BiologÃa Computacional y de Sistemas Clasificación: 006.37 Resumen: Este libro constituye las actas del 12.º Taller internacional sobre aprendizaje automático en imágenes médicas, MLMI 2021, celebrado junto con MICCAI 2021, en Estrasburgo, Francia, en septiembre de 2021.* Los 71 artÃculos presentados en este volumen fueron cuidadosamente revisados ​​y seleccionados de 92 presentaciones. Se centran en las principales tendencias y desafÃos en el área mencionada anteriormente, con el objetivo de identificar nuevas técnicas de vanguardia y sus usos en imágenes médicas. Los temas tratados son: aprendizaje profundo, aprendizaje generativo adversario, aprendizaje conjunto, aprendizaje disperso, aprendizaje multitarea, aprendizaje multivista, aprendizaje múltiple y aprendizaje por refuerzo, con sus aplicaciones al análisis de imágenes médicas, detección y diagnóstico asistido por computadora, fusión multimodal, reconstrucción de imágenes, recuperación de imágenes, análisis de imágenes celulares, imágenes moleculares, patologÃa digital, etc. *El taller se realizó de manera virtual. Tipo de medio : Computadora Summary : This book constitutes the proceedings of the 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.* The 71 papers presented in this volume were carefully reviewed and selected from 92 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. *The workshop was held virtually. Enlace de acceso : https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
