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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 : 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 : 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
006.6 |
| 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] ] : 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 : 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 : 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] ] : 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 : 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 : 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 | ||
| ||
Machine Learning in Medical Imaging [[electronic resource] ] : 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings / / edited by Luping Zhou, Li Wang, Qian Wang, Yinghuan Shi
| Machine Learning in Medical Imaging [[electronic resource] ] : 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings / / edited by Luping Zhou, Li Wang, Qian Wang, Yinghuan Shi |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XII, 341 p. 128 illus.) |
| Disciplina | 616.07540285 |
| 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 | 3-319-24888-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466302803316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Machine Learning in Medical Imaging : 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings / / edited by Luping Zhou, Li Wang, Qian Wang, Yinghuan Shi
| Machine Learning in Medical Imaging : 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015, Proceedings / / edited by Luping Zhou, Li Wang, Qian Wang, Yinghuan Shi |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XII, 341 p. 128 illus.) |
| Disciplina | 616.07540285 |
| 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 | 3-319-24888-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483714403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||