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Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, Lena Maier-Hein, João Manuel R.S. Tavares, Andrew Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi
| Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, Lena Maier-Hein, João Manuel R.S. Tavares, Andrew Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XVII, 387 p. 159 illus.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Health informatics Education—Data processing Application software Computer security Artificial Intelligence Health Informatics Computers and Education Computer Appl. in Social and Behavioral Sciences Systems and Data Security |
| ISBN | 3-030-00889-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior -- Weakly Supervised Localisation for Fetal Ultrasound Images -- Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images -- Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks -- Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease -- Contextual Additive Networks to Efficiently Boost 3D Image Segmentations -- Longitudinal detection of radiological abnormalities with time-modulated LSTM -- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays -- Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy -- Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps -- Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images -- Deep semi-supervised segmentation with weight-averaged consistency targets -- Focal Dice Loss and Image Dilation for Brain Tumor Segmentation -- Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography -- Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection -- Automatic myocardial strain imaging in echocardiography using deep learning -- 3D Convolutional Neural Networks for Classification of Functional Connectomes -- Computed Tomography Image Enhancement using 3D Convolutional Neural Network -- Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning -- A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data -- Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes -- Learning to Segment Medical Images with Scribble-Supervision Alone -- Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration -- TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees -- Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation -- UOLO - automatic object detection and segmentation in biomedical images -- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks -- Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification -- Nonlinear adaptively learned optimization for object localization in 3D medical images -- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network -- UNet++: A Nested U-Net Architecture for Medical Image Segmentation -- MTMR-Net: Multi-Task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis -- PIMMS: Permutation Invariant Multi-Modal Segmentation -- Handling Missing Annotations for Semantic Segmentation with Deep ConvNets -- 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation -- ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans -- Unpaired Deep Cross-modality Synthesis with Fast Training -- Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification -- Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN -- A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI -- Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson’s Disease -- Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features -- Integrating deformable modeling with 3D deep neural network segmentation. |
| Record Nr. | UNINA-9910349404403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, Lena Maier-Hein, João Manuel R.S. Tavares, Andrew Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi
| Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings / / edited by Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, Lena Maier-Hein, João Manuel R.S. Tavares, Andrew Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XVII, 387 p. 159 illus.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Health informatics Education—Data processing Application software Computer security Artificial Intelligence Health Informatics Computers and Education Computer Appl. in Social and Behavioral Sciences Systems and Data Security |
| ISBN | 3-030-00889-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior -- Weakly Supervised Localisation for Fetal Ultrasound Images -- Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images -- Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks -- Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease -- Contextual Additive Networks to Efficiently Boost 3D Image Segmentations -- Longitudinal detection of radiological abnormalities with time-modulated LSTM -- SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays -- Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy -- Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps -- Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images -- Deep semi-supervised segmentation with weight-averaged consistency targets -- Focal Dice Loss and Image Dilation for Brain Tumor Segmentation -- Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography -- Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection -- Automatic myocardial strain imaging in echocardiography using deep learning -- 3D Convolutional Neural Networks for Classification of Functional Connectomes -- Computed Tomography Image Enhancement using 3D Convolutional Neural Network -- Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning -- A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data -- Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes -- Learning to Segment Medical Images with Scribble-Supervision Alone -- Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration -- TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees -- Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation -- UOLO - automatic object detection and segmentation in biomedical images -- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks -- Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification -- Nonlinear adaptively learned optimization for object localization in 3D medical images -- Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network -- UNet++: A Nested U-Net Architecture for Medical Image Segmentation -- MTMR-Net: Multi-Task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis -- PIMMS: Permutation Invariant Multi-Modal Segmentation -- Handling Missing Annotations for Semantic Segmentation with Deep ConvNets -- 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation -- ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans -- Unpaired Deep Cross-modality Synthesis with Fast Training -- Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification -- Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN -- A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI -- Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson’s Disease -- Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features -- Integrating deformable modeling with 3D deep neural network segmentation. |
| Record Nr. | UNISA-996466201603316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu
| Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIX, 385 p. 169 illus.) |
| Disciplina | 006.42 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Health informatics Bioinformatics Logic design Image Processing and Computer Vision Artificial Intelligence Health Informatics Computational Biology/Bioinformatics Logic Design |
| ISBN | 3-319-67558-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Workshop Editors -- Preface DLMIA 2017 -- Organization -- Preface ML-CDS 2017 -- Organization -- Contents -- Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 -- Simultaneous Multiple Surface Segmentation Using Deep Learning -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Results -- 5 Discussion and Conclusion -- References -- A Deep Residual Inception Network for HEp-2 Cell Classification -- Abstract -- 1 Introduction -- 2 Deep Residual Inception -- 2.1 Network Architecture -- 2.2 DRI Module -- 2.3 Network Training -- 3 Results -- 3.1 Dataset -- 3.2 Data Augmentation -- 3.3 Performance Analysis -- 3.4 Comparisons -- 4 Conclusion -- References -- Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures -- 1 Introduction -- 2 Method -- 2.1 SharpMask Feature Fusion Architecture and CRF Refinement -- 2.2 Learning Anatomical Constraints -- 3 Experiments -- 3.1 Dataset and Pre-processing -- 3.2 Training -- 3.3 Results -- 4 Conclusions -- References -- Accelerated Magnetic Resonance Imaging by Adversarial Neural Network -- 1 Introduction -- 2 Method -- 2.1 K-space -- 2.2 Objective -- 2.3 Network Architecture -- 3 Experimental Results -- 4 Conclusions -- References -- Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preprocessing -- 3.2 Fully Convolutional Network -- 3.3 Shape Constraints -- 4 Experiments -- 5 Conclusion -- References -- 3D Randomized Connection Network with Graph-Based Inference -- 1 Introduction -- 2 Methodology -- 2.1 Convolutional LSTM and 3D Convolution -- 2.2 Randomized Connection Network -- 2.3 Graph-Based Inference -- 3 Experiment -- 4 Conclusion -- References -- Adversarial Training and Dilated Convolutions for Brain MRI Segmentation -- 1 Introduction.
2 Materials and Methods -- 2.1 Data -- 2.2 Network Architecture -- 2.3 Adversarial Training -- 3 Experiments and Results -- 3.1 Experiments -- 3.2 Evaluation -- 4 Discussion and Conclusions -- References -- CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Preparation -- 3.2 Network Architecture and Training -- 3.3 Three Approaches to Drusen Segmentation -- 4 Experiments and Results -- 4.1 Cross-Validation Setup -- 4.2 Quantitative Evaluation -- 4.3 Robustness to Additional Pathology -- 4.4 3D Visualization of Results -- 5 Conclusion -- References -- Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Localization Ground Truth -- 3.2 Network Architectures -- 3.3 Training -- 3.4 Region-Aware Term -- 3.5 Updated Loss Function -- 3.6 Experiments and Inference -- 4 Results and Discussions -- 5 Conclusion -- References -- Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 The Underlying CNN Architecture -- 2.3 Three Approaches to Handling Appearance Variability -- 2.4 Evaluation -- 3 Experiments and Results -- 4 Discussion and Conclusions -- References -- Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks -- 1 Introduction -- 2 Methods -- 2.1 Lung Segmentation with Atrous Convolutions -- 2.2 Network-Wise Training of CNN -- 3 Computational Experiments -- 3.1 Performance Metrics -- 3.2 Quantatitive and Qualititive Results -- 4 Conclusion -- References -- Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms -- 1 Introduction -- 2 Methods -- 2.1 Dataset. 2.2 Deep Residual Recurrent Neural Networks (RRNs) -- 3 Experiments -- 4 Results and Discussion -- 5 Conclusion and Future Works -- References -- Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusion -- References -- Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression -- 1 Introduction -- 2 Methods -- 2.1 Regression-Based 2D-3D Registration -- 2.2 CNN Based Regressor -- 3 Experiments -- 3.1 Qualitative Assessment -- 4 Conclusion -- References -- A Deep Level Set Method for Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 The Level Set Method -- 2.2 The Integrated FCN-Levelset Model -- 3 Experiments and Results -- 3.1 Data -- 3.2 Experiments -- 3.3 Results -- 4 Discussion -- References -- Context-Based Normalization of Histological Stains Using Deep Convolutional Features -- 1 Introduction -- 2 Method -- 2.1 Feature-Aware Normalization -- 2.2 Normalization by Denoising -- 3 Experiments -- 4 Discussion -- References -- Transitioning Between Convolutional and Fully Connected Layers in Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Inception Module -- 3.2 Transition Module -- 4 Experiment -- 5 Results -- 5.1 Experiment 1: Comparison with Regularizers -- 5.2 Experiment 2: Comparing Architectures -- 5.3 Experiment 3: BreaKHis -- 6 Conclusion -- References -- Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Compute Mean Gray Matter CBF per Anatomical Region -- 3.2 Identify candidate regions for further analysis -- 3.3 Estimate Candidate Region Association Using a DNN -- 4 Results -- 4.1 Performance Comparison of the Learning Models -- 4.2 Statistical Significance of the Proposed Model -- 5 Discussion -- 6 Conclusions. References -- Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning -- 1 Introduction -- 2 Method -- 2.1 Multi-task Learning -- 2.2 Multi-task Deep Learning for Incomplete Multimodal Data -- 3 Materials, Preprocessing and Feature Extraction -- 4 Results and Discussions -- 5 Conclusion -- References -- A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification -- 1 Introduction -- 2 Multi-scale CNN with Curriculum Learning Strategy -- 3 Experiments -- 4 Conclusions -- References -- Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning -- 1 Introduction -- 2 The ShonitSystem for Analysis of Peripheral Blood Smears -- 3 Deep Learning Techniques for Analyzing PBS Images -- 3.1 Cell Extraction -- 3.2 Cell Classification -- 4 Experimental Results -- 5 Conclusion -- References -- AGNet: Attention-Guided Network for Surgical Tool Presence Detection -- 1 Introduction -- 2 Attention-Guided Network -- 2.1 Global Prediction Network -- 2.2 Local Prediction Network -- 3 Experiments -- 3.1 Datasets and Preprocessing -- 3.2 Training Procedure -- 3.3 Ablation Analysis -- 3.4 Comparison with the State-of-the-Arts -- 4 Conclusion -- References -- Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker -- 1 Introduction -- 2 Method -- 2.1 Lobar Boundary Segmentation -- 2.2 3D Random Walker -- 3 Experiments and Results -- 4 Conclusion -- References -- End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network -- 1 Introduction -- 2 Method -- 3 Data -- 4 Experiments and Results -- 4.1 Registration of Handwritten Digits -- 4.2 Registration of Cardiac MRI -- 5 Discussion and Conclusion -- References. Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Method -- 4.1 Patch Generation -- 4.2 CNN Architecture -- 4.3 Training and Testing Workflow -- 5 Results and Discussion -- 6 Conclusion -- References -- 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Pancreas Localization -- 2.3 Patient-Specific Probabilistic Atlas Generation and Pancreas Segmentation -- 3 Experiments and Discussion -- References -- A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology -- 1 Introduction -- 2 Methodology -- 2.1 Whole Slide Image Handling -- 2.2 Deep Convolutional Neural Networks Based Mitosis Detection -- 2.3 Tumor Proliferation Score Prediction -- 3 Results -- 3.1 Datasets -- 3.2 Experiments -- 4 Conclusion -- References -- Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations -- 1 Introduction -- 2 Methods -- 2.1 Loss Functions for Unbalanced Data -- 2.2 Deep Learning Framework -- 3 Experiments and Results -- 3.1 Experiments -- 3.2 2D Results -- 3.3 3D Results -- 4 Discussion -- References -- ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features -- 1 Introduction -- 2 Method -- 2.1 Feature Generation Using a Convolutional Autoencoder -- 2.2 Deformable Image Registration Using a Spatial Transformer Network -- 3 Results -- 4 Discussion and Conclusion -- References -- Fully Convolutional Regression Network for Accurate Detection of Measurement Points -- 1 Introduction -- 2 Related Work -- 3 Regressing Point Locations -- 3.1 Fully Convolutional Network with Center of Mass Layer -- 3.2 Convolutional Long Short-Term Memory for Temporal Consistency -- 4 Results. 5 Conclusion. |
| Record Nr. | UNISA-996465975403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu
| Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings / / edited by M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIX, 385 p. 169 illus.) |
| Disciplina | 006.42 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Optical data processing
Artificial intelligence Health informatics Bioinformatics Logic design Image Processing and Computer Vision Artificial Intelligence Health Informatics Computational Biology/Bioinformatics Logic Design |
| ISBN | 3-319-67558-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Workshop Editors -- Preface DLMIA 2017 -- Organization -- Preface ML-CDS 2017 -- Organization -- Contents -- Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 -- Simultaneous Multiple Surface Segmentation Using Deep Learning -- 1 Introduction -- 2 Method -- 3 Experiments -- 4 Results -- 5 Discussion and Conclusion -- References -- A Deep Residual Inception Network for HEp-2 Cell Classification -- Abstract -- 1 Introduction -- 2 Deep Residual Inception -- 2.1 Network Architecture -- 2.2 DRI Module -- 2.3 Network Training -- 3 Results -- 3.1 Dataset -- 3.2 Data Augmentation -- 3.3 Performance Analysis -- 3.4 Comparisons -- 4 Conclusion -- References -- Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures -- 1 Introduction -- 2 Method -- 2.1 SharpMask Feature Fusion Architecture and CRF Refinement -- 2.2 Learning Anatomical Constraints -- 3 Experiments -- 3.1 Dataset and Pre-processing -- 3.2 Training -- 3.3 Results -- 4 Conclusions -- References -- Accelerated Magnetic Resonance Imaging by Adversarial Neural Network -- 1 Introduction -- 2 Method -- 2.1 K-space -- 2.2 Objective -- 2.3 Network Architecture -- 3 Experimental Results -- 4 Conclusions -- References -- Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Preprocessing -- 3.2 Fully Convolutional Network -- 3.3 Shape Constraints -- 4 Experiments -- 5 Conclusion -- References -- 3D Randomized Connection Network with Graph-Based Inference -- 1 Introduction -- 2 Methodology -- 2.1 Convolutional LSTM and 3D Convolution -- 2.2 Randomized Connection Network -- 2.3 Graph-Based Inference -- 3 Experiment -- 4 Conclusion -- References -- Adversarial Training and Dilated Convolutions for Brain MRI Segmentation -- 1 Introduction.
2 Materials and Methods -- 2.1 Data -- 2.2 Network Architecture -- 2.3 Adversarial Training -- 3 Experiments and Results -- 3.1 Experiments -- 3.2 Evaluation -- 4 Discussion and Conclusions -- References -- CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Data Preparation -- 3.2 Network Architecture and Training -- 3.3 Three Approaches to Drusen Segmentation -- 4 Experiments and Results -- 4.1 Cross-Validation Setup -- 4.2 Quantitative Evaluation -- 4.3 Robustness to Additional Pathology -- 4.4 3D Visualization of Results -- 5 Conclusion -- References -- Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 Localization Ground Truth -- 3.2 Network Architectures -- 3.3 Training -- 3.4 Region-Aware Term -- 3.5 Updated Loss Function -- 3.6 Experiments and Inference -- 4 Results and Discussions -- 5 Conclusion -- References -- Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 The Underlying CNN Architecture -- 2.3 Three Approaches to Handling Appearance Variability -- 2.4 Evaluation -- 3 Experiments and Results -- 4 Discussion and Conclusions -- References -- Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks -- 1 Introduction -- 2 Methods -- 2.1 Lung Segmentation with Atrous Convolutions -- 2.2 Network-Wise Training of CNN -- 3 Computational Experiments -- 3.1 Performance Metrics -- 3.2 Quantatitive and Qualititive Results -- 4 Conclusion -- References -- Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms -- 1 Introduction -- 2 Methods -- 2.1 Dataset. 2.2 Deep Residual Recurrent Neural Networks (RRNs) -- 3 Experiments -- 4 Results and Discussion -- 5 Conclusion and Future Works -- References -- Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusion -- References -- Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression -- 1 Introduction -- 2 Methods -- 2.1 Regression-Based 2D-3D Registration -- 2.2 CNN Based Regressor -- 3 Experiments -- 3.1 Qualitative Assessment -- 4 Conclusion -- References -- A Deep Level Set Method for Image Segmentation -- 1 Introduction -- 2 Methods -- 2.1 The Level Set Method -- 2.2 The Integrated FCN-Levelset Model -- 3 Experiments and Results -- 3.1 Data -- 3.2 Experiments -- 3.3 Results -- 4 Discussion -- References -- Context-Based Normalization of Histological Stains Using Deep Convolutional Features -- 1 Introduction -- 2 Method -- 2.1 Feature-Aware Normalization -- 2.2 Normalization by Denoising -- 3 Experiments -- 4 Discussion -- References -- Transitioning Between Convolutional and Fully Connected Layers in Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Inception Module -- 3.2 Transition Module -- 4 Experiment -- 5 Results -- 5.1 Experiment 1: Comparison with Regularizers -- 5.2 Experiment 2: Comparing Architectures -- 5.3 Experiment 3: BreaKHis -- 6 Conclusion -- References -- Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Compute Mean Gray Matter CBF per Anatomical Region -- 3.2 Identify candidate regions for further analysis -- 3.3 Estimate Candidate Region Association Using a DNN -- 4 Results -- 4.1 Performance Comparison of the Learning Models -- 4.2 Statistical Significance of the Proposed Model -- 5 Discussion -- 6 Conclusions. References -- Multi-stage Diagnosis of Alzheimer's Disease with Incomplete Multimodal Data via Multi-task Deep Learning -- 1 Introduction -- 2 Method -- 2.1 Multi-task Learning -- 2.2 Multi-task Deep Learning for Incomplete Multimodal Data -- 3 Materials, Preprocessing and Feature Extraction -- 4 Results and Discussions -- 5 Conclusion -- References -- A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification -- 1 Introduction -- 2 Multi-scale CNN with Curriculum Learning Strategy -- 3 Experiments -- 4 Conclusions -- References -- Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning -- 1 Introduction -- 2 The ShonitSystem for Analysis of Peripheral Blood Smears -- 3 Deep Learning Techniques for Analyzing PBS Images -- 3.1 Cell Extraction -- 3.2 Cell Classification -- 4 Experimental Results -- 5 Conclusion -- References -- AGNet: Attention-Guided Network for Surgical Tool Presence Detection -- 1 Introduction -- 2 Attention-Guided Network -- 2.1 Global Prediction Network -- 2.2 Local Prediction Network -- 3 Experiments -- 3.1 Datasets and Preprocessing -- 3.2 Training Procedure -- 3.3 Ablation Analysis -- 3.4 Comparison with the State-of-the-Arts -- 4 Conclusion -- References -- Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker -- 1 Introduction -- 2 Method -- 2.1 Lobar Boundary Segmentation -- 2.2 3D Random Walker -- 3 Experiments and Results -- 4 Conclusion -- References -- End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network -- 1 Introduction -- 2 Method -- 3 Data -- 4 Experiments and Results -- 4.1 Registration of Handwritten Digits -- 4.2 Registration of Cardiac MRI -- 5 Discussion and Conclusion -- References. Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Method -- 4.1 Patch Generation -- 4.2 CNN Architecture -- 4.3 Training and Testing Workflow -- 5 Results and Discussion -- 6 Conclusion -- References -- 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation -- 1 Introduction -- 2 Method -- 2.1 Overview -- 2.2 Pancreas Localization -- 2.3 Patient-Specific Probabilistic Atlas Generation and Pancreas Segmentation -- 3 Experiments and Discussion -- References -- A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology -- 1 Introduction -- 2 Methodology -- 2.1 Whole Slide Image Handling -- 2.2 Deep Convolutional Neural Networks Based Mitosis Detection -- 2.3 Tumor Proliferation Score Prediction -- 3 Results -- 3.1 Datasets -- 3.2 Experiments -- 4 Conclusion -- References -- Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations -- 1 Introduction -- 2 Methods -- 2.1 Loss Functions for Unbalanced Data -- 2.2 Deep Learning Framework -- 3 Experiments and Results -- 3.1 Experiments -- 3.2 2D Results -- 3.3 3D Results -- 4 Discussion -- References -- ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features -- 1 Introduction -- 2 Method -- 2.1 Feature Generation Using a Convolutional Autoencoder -- 2.2 Deformable Image Registration Using a Spatial Transformer Network -- 3 Results -- 4 Discussion and Conclusion -- References -- Fully Convolutional Regression Network for Accurate Detection of Measurement Points -- 1 Introduction -- 2 Related Work -- 3 Regressing Point Locations -- 3.1 Fully Convolutional Network with Center of Mass Layer -- 3.2 Convolutional Long Short-Term Memory for Temporal Consistency -- 4 Results. 5 Conclusion. |
| Record Nr. | UNINA-9910484561103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi
| Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (xvi, 93 pages) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Mathematical logic Health informatics Optical data processing Artificial Intelligence Mathematical Logic and Formal Languages Health Informatics Image Processing and Computer Vision |
| ISBN | 3-030-33850-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019) -- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification -- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics -- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis -- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection -- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules -- Deep neural network or dermatologist? -- Towards Interpretability of Segmentation Networks by analyzing DeepDreams -- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019) -- Towards Automatic Diagnosis from Multi-modal Medical Data -- Deep Learning based Multi-Modal Registration for Retinal Imaging -- Automated Enriched Medical Concept Generation for Chest X-ray Images. |
| Record Nr. | UNINA-9910349269203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi
| Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support [[electronic resource] ] : Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / / edited by Kenji Suzuki, Mauricio Reyes, Tanveer Syeda-Mahmood, Ender Konukoglu, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (xvi, 93 pages) |
| Disciplina | 616.07540285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Artificial intelligence
Mathematical logic Health informatics Optical data processing Artificial Intelligence Mathematical Logic and Formal Languages Health Informatics Image Processing and Computer Vision |
| ISBN | 3-030-33850-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019) -- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification -- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics -- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis -- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection -- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules -- Deep neural network or dermatologist? -- Towards Interpretability of Segmentation Networks by analyzing DeepDreams -- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019) -- Towards Automatic Diagnosis from Multi-modal Medical Data -- Deep Learning based Multi-Modal Registration for Retinal Imaging -- Automated Enriched Medical Concept Generation for Chest X-ray Images. |
| Record Nr. | UNISA-996466310703316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical Content-Based Retrieval for Clinical Decision Support [[electronic resource] ] : Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1st, 2012, Revised Selected Papers / / edited by Hayit Greenspan, Henning Müller, Tanveer Syeda-Mahmood
| Medical Content-Based Retrieval for Clinical Decision Support [[electronic resource] ] : Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1st, 2012, Revised Selected Papers / / edited by Hayit Greenspan, Henning Müller, Tanveer Syeda-Mahmood |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (VIII, 145 p. 41 illus.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Information storage and retrieval
Application software Pattern recognition Data mining Optical data processing Management information systems Computer science Information Storage and Retrieval Information Systems Applications (incl. Internet) Pattern Recognition Data Mining and Knowledge Discovery Image Processing and Computer Vision Management of Computing and Information Systems |
| ISBN | 3-642-36678-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Workshop Overview -- Overview of the Third Workshop on Medical Content–Based Retrieval for Clinical Decision Support (MCBR–CDS 2012) -- Invited Talk -- A Polynomial Model of Surgical Gestures for Real-Time Retrieval of Surgery Videos -- Methods -- Exploiting 3D Part-Based Analysis, Description and Indexing to Support Medical Applications -- Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models -- 3D/4D Data Retrieval -- Retrieval of 4D Dual Energy CT for Pulmonary Embolism Diagnosis -- Immediate ROI Search for 3-D Medical Images -- The Synergy of 3D SIFT and Sparse Codes for Classification of Viewpoints from Echocardiogram Videos -- Assessing the Classification of Liver Focal Lesions by Using Multi-phase Computer Tomography Scans -- Invited Talk -- VISCERAL: Towards Large Data in Medical Imaging — Challenges and Directions -- Visual Features -- Customised Frequency Pre-filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images -- Bag–of–Colors for Biomedical Document Image Classification -- Multimodal Retrieval -- An SVD–Bypass Latent Semantic Analysis for Image Retrieval -- Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes. |
| Record Nr. | UNISA-996466265503316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Medical Content-Based Retrieval for Clinical Decision Support [[electronic resource] ] : Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1st, 2012, Revised Selected Papers / / edited by Hayit Greenspan, Henning Müller, Tanveer Syeda-Mahmood
| Medical Content-Based Retrieval for Clinical Decision Support [[electronic resource] ] : Third MICCAI International Workshop, MCBR-CDS 2012, Nice, France, October 1st, 2012, Revised Selected Papers / / edited by Hayit Greenspan, Henning Müller, Tanveer Syeda-Mahmood |
| Edizione | [1st ed. 2013.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
| Descrizione fisica | 1 online resource (VIII, 145 p. 41 illus.) |
| Disciplina | 610.285 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Information storage and retrieval
Application software Pattern recognition Data mining Optical data processing Management information systems Computer science Information Storage and Retrieval Information Systems Applications (incl. Internet) Pattern Recognition Data Mining and Knowledge Discovery Image Processing and Computer Vision Management of Computing and Information Systems |
| ISBN | 3-642-36678-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Workshop Overview -- Overview of the Third Workshop on Medical Content–Based Retrieval for Clinical Decision Support (MCBR–CDS 2012) -- Invited Talk -- A Polynomial Model of Surgical Gestures for Real-Time Retrieval of Surgery Videos -- Methods -- Exploiting 3D Part-Based Analysis, Description and Indexing to Support Medical Applications -- Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models -- 3D/4D Data Retrieval -- Retrieval of 4D Dual Energy CT for Pulmonary Embolism Diagnosis -- Immediate ROI Search for 3-D Medical Images -- The Synergy of 3D SIFT and Sparse Codes for Classification of Viewpoints from Echocardiogram Videos -- Assessing the Classification of Liver Focal Lesions by Using Multi-phase Computer Tomography Scans -- Invited Talk -- VISCERAL: Towards Large Data in Medical Imaging — Challenges and Directions -- Visual Features -- Customised Frequency Pre-filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images -- Bag–of–Colors for Biomedical Document Image Classification -- Multimodal Retrieval -- An SVD–Bypass Latent Semantic Analysis for Image Retrieval -- Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes. |
| Record Nr. | UNINA-9910483182803321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Medical Content-Based Retrieval for Clinical Decision Support [[electronic resource] ] : Second MICCAI International Workshop, MCBR-CDS 2011, Toronto, Canada, September 22, 2011, Revised Selected Papers / / edited by Henning Mueller, Hayit Greenspan, Tanveer Syeda-Mahmood
| Medical Content-Based Retrieval for Clinical Decision Support [[electronic resource] ] : Second MICCAI International Workshop, MCBR-CDS 2011, Toronto, Canada, September 22, 2011, Revised Selected Papers / / edited by Henning Mueller, Hayit Greenspan, Tanveer Syeda-Mahmood |
| Edizione | [1st ed. 2012.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 |
| Descrizione fisica | 1 online resource (X, 153 p.) |
| Disciplina | 006.312 |
| Collana | Image Processing, Computer Vision, Pattern Recognition, and Graphics |
| Soggetto topico |
Data mining
Biometrics (Biology) Pattern recognition Information storage and retrieval Optical data processing Management information systems Computer science Data Mining and Knowledge Discovery Biometrics Pattern Recognition Information Storage and Retrieval Computer Imaging, Vision, Pattern Recognition and Graphics Management of Computing and Information Systems |
| ISBN | 3-642-28460-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465943203316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 [[electronic resource] ] : 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VIII / / edited by Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
| Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 [[electronic resource] ] : 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VIII / / edited by Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor |
| Autore | Greenspan Hayit |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (726 pages) |
| Disciplina | 006 |
| Altri autori (Persone) |
MadabhushiAnant
MousaviParvin SalcudeanSeptimiu DuncanJames Syeda-MahmoodTanveer TaylorRussell |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing - Digital techniques
Computer vision Application software Machine learning Education - Data processing Social sciences - Data processing Biomedical engineering Computer Imaging, Vision, Pattern Recognition and Graphics Computer and Information Systems Applications Machine Learning Computers and Education Computer Application in Social and Behavioral Sciences Biomedical Engineering and Bioengineering |
| ISBN | 3-031-43993-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910746956803321 |
Greenspan Hayit
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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