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 : 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] ] : 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 : 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 | ||
|