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Machine Learning in Medical Imaging [[electronic resource] ] : 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Heung-Il Suk, Mingxia Liu, Pingkun Yan, Chunfeng Lian
| Machine Learning in Medical Imaging [[electronic resource] ] : 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Heung-Il Suk, Mingxia Liu, Pingkun Yan, Chunfeng Lian |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVIII, 695 p. 310 illus., 245 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Image Processing and Computer Vision Artificial Intelligence |
| ISBN | 3-030-32692-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 Memory Models 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. |
| Record Nr. | UNISA-996466192503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Heung-Il Suk, Mingxia Liu, Pingkun Yan, Chunfeng Lian
| Machine Learning in Medical Imaging [[electronic resource] ] : 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings / / edited by Heung-Il Suk, Mingxia Liu, Pingkun Yan, Chunfeng Lian |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XVIII, 695 p. 310 illus., 245 illus. in color.) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Image Processing and Computer Vision Artificial Intelligence |
| ISBN | 3-030-32692-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 Memory Models 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. |
| Record Nr. | UNINA-9910349274203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Yinghuan Shi, Heung-Il Suk, Mingxia Liu
| Machine Learning in Medical Imaging [[electronic resource] ] : 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Yinghuan Shi, Heung-Il Suk, Mingxia Liu |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIII, 409 p. 152 illus.) |
| Disciplina | 006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Health informatics Data mining Image Processing and Computer Vision Artificial Intelligence Health Informatics Data Mining and Knowledge Discovery |
| ISBN | 3-030-00919-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910349403703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Yinghuan Shi, Heung-Il Suk, Mingxia Liu
| Machine Learning in Medical Imaging [[electronic resource] ] : 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / / edited by Yinghuan Shi, Heung-Il Suk, Mingxia Liu |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIII, 409 p. 152 illus.) |
| Disciplina | 006.37 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Health informatics Data mining Image Processing and Computer Vision Artificial Intelligence Health Informatics Data Mining and Knowledge Discovery |
| ISBN | 3-030-00919-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466333803316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings / / edited by Qian Wang, Yinghuan Shi, Heung-Il Suk, Kenji Suzuki
| Machine Learning in Medical Imaging [[electronic resource] ] : 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings / / edited by Qian Wang, Yinghuan Shi, Heung-Il Suk, Kenji Suzuki |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XV, 391 p. 134 illus.) |
| Disciplina | 006.31 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Software engineering Health informatics Data mining Artificial intelligence Image Processing and Computer Vision Software Engineering/Programming and Operating Systems Health Informatics Data Mining and Knowledge Discovery Artificial Intelligence |
| ISBN | 3-319-67389-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From Large to Small Organ Segmentation in CT Using Regional Context -- Motion Corruption Detection in Breast DCE-MRI -- Detection and Localization of Drosophila Egg Chambers in Microscopy Images -- Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring -- Atlas of Classifiers for Brain MRI Segmentation -- Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis -- Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer’s Disease -- Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes -- Automatic Classification of Proximal Femur Fractures Based on Attention Models -- Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation -- Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble -- STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion -- Classification of Alzheimer’s Disease by Cascaded Convolutional Neural Networks Using PET Images -- Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images -- Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base -- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis -- 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels -- Efficient Groupwise Registration for Brain MRI by Fast Initialization -- Sparse Multi-View Task-centralized Learning for ASD Diagnosis -- Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis -- Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data -- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images -- Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity -- Gradient Boosted Trees for Corrective Learning -- Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis -- A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling -- Collage CNN for Renal Cell Carcinoma Detection from CT -- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images -- Localizing Cardiac Structures in Fetal Heart Ultrasound Video -- Deformable Registration Through Learning of Context-Specific Metric Aggregation -- Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning Based Cascade Framework -- 3D U-net with Multi-Level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images -- Indecisive Trees for Classification and Prediction of Knee Osteoarthritis -- Whole Brain Segmentation and Labeling from CT using synthetic MR Images -- Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification -- Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification -- Novel Effective Connectivity Network Inference for MCI Identification -- Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network -- Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images -- Deep-Fext: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction -- Product Space Decompositions for Continuous Representations of Brain Connectivity -- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks -- Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging -- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. |
| Record Nr. | UNINA-9910484379503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings / / edited by Qian Wang, Yinghuan Shi, Heung-Il Suk, Kenji Suzuki
| Machine Learning in Medical Imaging [[electronic resource] ] : 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings / / edited by Qian Wang, Yinghuan Shi, Heung-Il Suk, Kenji Suzuki |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XV, 391 p. 134 illus.) |
| Disciplina | 006.31 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Software engineering Health informatics Data mining Artificial intelligence Image Processing and Computer Vision Software Engineering/Programming and Operating Systems Health Informatics Data Mining and Knowledge Discovery Artificial Intelligence |
| ISBN | 3-319-67389-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From Large to Small Organ Segmentation in CT Using Regional Context -- Motion Corruption Detection in Breast DCE-MRI -- Detection and Localization of Drosophila Egg Chambers in Microscopy Images -- Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring -- Atlas of Classifiers for Brain MRI Segmentation -- Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis -- Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer’s Disease -- Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes -- Automatic Classification of Proximal Femur Fractures Based on Attention Models -- Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation -- Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble -- STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion -- Classification of Alzheimer’s Disease by Cascaded Convolutional Neural Networks Using PET Images -- Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images -- Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base -- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis -- 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels -- Efficient Groupwise Registration for Brain MRI by Fast Initialization -- Sparse Multi-View Task-centralized Learning for ASD Diagnosis -- Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis -- Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data -- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images -- Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity -- Gradient Boosted Trees for Corrective Learning -- Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis -- A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling -- Collage CNN for Renal Cell Carcinoma Detection from CT -- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images -- Localizing Cardiac Structures in Fetal Heart Ultrasound Video -- Deformable Registration Through Learning of Context-Specific Metric Aggregation -- Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning Based Cascade Framework -- 3D U-net with Multi-Level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images -- Indecisive Trees for Classification and Prediction of Knee Osteoarthritis -- Whole Brain Segmentation and Labeling from CT using synthetic MR Images -- Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification -- Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification -- Novel Effective Connectivity Network Inference for MCI Identification -- Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network -- Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images -- Deep-Fext: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction -- Product Space Decompositions for Continuous Representations of Brain Connectivity -- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks -- Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging -- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. |
| Record Nr. | UNISA-996465979603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings / / edited by Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk
| Machine Learning in Medical Imaging [[electronic resource] ] : 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings / / edited by Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIV, 324 p. 127 illus.) |
| Disciplina | 006.6 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Pattern recognition Health informatics Data mining Artificial intelligence Image Processing and Computer Vision Pattern Recognition Health Informatics Data Mining and Knowledge Discovery Artificial Intelligence |
| ISBN | 3-319-47157-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465662703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings / / edited by Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk
| Machine Learning in Medical Imaging [[electronic resource] ] : 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings / / edited by Li Wang, Ehsan Adeli, Qian Wang, Yinghuan Shi, Heung-Il Suk |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIV, 324 p. 127 illus.) |
| Disciplina | 006.6 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Pattern recognition Health informatics Data mining Artificial intelligence Image Processing and Computer Vision Pattern Recognition Health Informatics Data Mining and Knowledge Discovery Artificial Intelligence |
| ISBN | 3-319-47157-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484925303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||