Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Ophthalmic Medical Image Analysis : 10th International Workshop, OMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / Bhavna Antony [and five others], editors
| Ophthalmic Medical Image Analysis : 10th International Workshop, OMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / Bhavna Antony [and five others], editors |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (174 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Computer Science Series |
| Soggetto topico | Machine learning |
| ISBN | 3-031-44013-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Associations Between Retinal Microvasculature Changes and Gray Matter Volume in a Mid-Life Cohort at Risk of Developing Alzheimer's Disease -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Image Processing -- 2.3 Statistical Analysis -- 3 Results -- 4 Discussion -- References -- Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images -- 1 Introduction -- 2 Methods -- 2.1 Model Architecture -- 2.2 Fusion Strategy -- 2.3 Manifold Mixup -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results and Discussion -- 4 Conclusion -- References -- Auxiliary-Domain Learning for a Functional Prediction of Glaucoma Progression -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition -- 2.2 Baseline Deep Learning Model -- 2.3 Hard Parameter Sharing and Combined Loss Function -- 3 Experiments and Results -- 4 Discussion -- 5 Conclusion -- References -- QuickQual: Lightweight, Convenient Retinal Image Quality Scoring with Off-the-Shelf Pretrained Models -- 1 Introduction -- 2 Methods -- 2.1 EyeQ Dataset -- 2.2 QuickQual -- 2.3 RIQS Beyond 3-Way Classification: Fixed Prior Linearisation -- 2.4 QuickQual MEga Minified Estimator (QuickQual-MEME) -- 2.5 Evaluation -- 3 Results -- 3.1 QuickQual Performance on EyeQ -- 3.2 QuickQual-MEME Performance on Binary Task -- 3.3 Convenience and Speed -- 4 Discussion -- References -- Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation -- 1 Introduction -- 2 Method -- 3 Results -- 4 Discussion and Conclusions -- References -- UAU-Net: United Attention U-Shaped Network for the Segmentation of Pigment Deposits in Fundus Images of Retinitis Pigmentosa -- 1 Introduction -- 2 Method -- 2.1 Proposed Model -- 2.2 Dataset -- 2.3 Evaluation Metrics.
3 Experiments and Results -- 3.1 Implementation Details -- 3.2 Ablation Study -- 3.3 Comparison Study -- 4 Conclusion -- References -- Glaucoma Progression Detection and Humphrey Visual Field Prediction Using Discriminative and Generative Vision Transformers -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 GP Detection Using TimeSformer -- 2.3 VF Prediction Using Generative ViT -- 3 Results and Discussion -- 3.1 GP Detection -- 3.2 VF Prediction Results -- 4 Discussion, Conclusions, and Future Directions -- References -- Utilizing Meta Pseudo Labels for Semantic Segmentation of Targeted Optic Nerve Features -- 1 Introduction -- 2 Meta Pseudo Labels -- 2.1 Implementation -- 3 Experimental Methods -- 3.1 Data Collection -- 3.2 Data Acquisition -- 3.3 Data Division -- 4 Results -- 4.1 Segmentation Results -- 4.2 Axon Counts -- 5 Discussion -- 5.1 Limitations -- 6 Conclusion -- References -- Privileged Modality Guided Network for Retinal Vessel Segmentation in Ultra-Wide-Field Images -- 1 Introduction -- 2 Methodology -- 2.1 Overall Architecture -- 2.2 Consistency Regularization Loss -- 2.3 Vessel Extraction Network -- 3 Experiments and Results -- 3.1 Data Description -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Comparisons with State-of-the-Art Methods -- 4 Conclusion -- References -- Adapting Segment Anything Model (SAM) for Retinal OCT -- 1 Introduction -- 2 Background and Related Works -- 3 Methods -- 4 Experimental Setup -- 4.1 Dataset and Evaluation -- 5 Results -- 6 Conclusion -- References -- Dual-Modality Grading of Keratoconus Severity Based on Corneal Topography and Clinical Indicators -- 1 Introduction -- 2 Method -- 2.1 Overall Architecture -- 2.2 Image Classifier on Corneal Topography -- 2.3 Data Classifier on Clinical Indicators -- 2.4 Fusion Strategy for Final Grading -- 3 Experiments and Results -- 3.1 Dataset. 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Results and Discussion -- 4 Conclusion -- References -- Automated Optic Disc Finder and Segmentation Using Deep Learning for Blood Flow Studies in the Eye -- 1 Introduction -- 2 Methods -- 2.1 Data Preparation -- 2.2 Model Training -- 2.3 Model Evaluations -- 3 Results -- 4 Discussion and Conclusion -- References -- Multi-relational Graph Convolutional Neural Networks for Carotid Artery Stenosis Diagnosis via Fundus Images -- 1 Introduction -- 2 Methodology -- 2.1 Feature Extraction Layer -- 2.2 Constructing Multi-graphs -- 2.3 M-GCNs -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Results -- 4 Conclusion -- References -- Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT: A PINNACLE Study Report -- 1 Introduction -- 1.1 Related Work -- 2 Methods: Predictive Model from Retinal OCT Volume -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- A Structure-Consistency GAN for Unpaired AS-OCT Image Inpainting -- 1 Introduction -- 2 Proposed Method -- 2.1 Network Architecture -- 2.2 Objective Function -- 3 Experiments -- 4 Conclusion -- References -- STAGE Challenge: Structural-Functional Transition in Glaucoma Assessment Challenge in MICCAI 2023 -- 1 Introduction -- 2 Dataset -- 3 Baseline -- 4 Evaluation -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996550556403316 |
| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Ophthalmic Medical Image Analysis : 10th International Workshop, OMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / Bhavna Antony [and five others], editors
| Ophthalmic Medical Image Analysis : 10th International Workshop, OMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings / / Bhavna Antony [and five others], editors |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (174 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Computer Science Series |
| Soggetto topico | Machine learning |
| ISBN | 3-031-44013-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Associations Between Retinal Microvasculature Changes and Gray Matter Volume in a Mid-Life Cohort at Risk of Developing Alzheimer's Disease -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Image Processing -- 2.3 Statistical Analysis -- 3 Results -- 4 Discussion -- References -- Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images -- 1 Introduction -- 2 Methods -- 2.1 Model Architecture -- 2.2 Fusion Strategy -- 2.3 Manifold Mixup -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results and Discussion -- 4 Conclusion -- References -- Auxiliary-Domain Learning for a Functional Prediction of Glaucoma Progression -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition -- 2.2 Baseline Deep Learning Model -- 2.3 Hard Parameter Sharing and Combined Loss Function -- 3 Experiments and Results -- 4 Discussion -- 5 Conclusion -- References -- QuickQual: Lightweight, Convenient Retinal Image Quality Scoring with Off-the-Shelf Pretrained Models -- 1 Introduction -- 2 Methods -- 2.1 EyeQ Dataset -- 2.2 QuickQual -- 2.3 RIQS Beyond 3-Way Classification: Fixed Prior Linearisation -- 2.4 QuickQual MEga Minified Estimator (QuickQual-MEME) -- 2.5 Evaluation -- 3 Results -- 3.1 QuickQual Performance on EyeQ -- 3.2 QuickQual-MEME Performance on Binary Task -- 3.3 Convenience and Speed -- 4 Discussion -- References -- Recurrent Self Fusion: Iterative Denoising for Consistent Retinal OCT Segmentation -- 1 Introduction -- 2 Method -- 3 Results -- 4 Discussion and Conclusions -- References -- UAU-Net: United Attention U-Shaped Network for the Segmentation of Pigment Deposits in Fundus Images of Retinitis Pigmentosa -- 1 Introduction -- 2 Method -- 2.1 Proposed Model -- 2.2 Dataset -- 2.3 Evaluation Metrics.
3 Experiments and Results -- 3.1 Implementation Details -- 3.2 Ablation Study -- 3.3 Comparison Study -- 4 Conclusion -- References -- Glaucoma Progression Detection and Humphrey Visual Field Prediction Using Discriminative and Generative Vision Transformers -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 GP Detection Using TimeSformer -- 2.3 VF Prediction Using Generative ViT -- 3 Results and Discussion -- 3.1 GP Detection -- 3.2 VF Prediction Results -- 4 Discussion, Conclusions, and Future Directions -- References -- Utilizing Meta Pseudo Labels for Semantic Segmentation of Targeted Optic Nerve Features -- 1 Introduction -- 2 Meta Pseudo Labels -- 2.1 Implementation -- 3 Experimental Methods -- 3.1 Data Collection -- 3.2 Data Acquisition -- 3.3 Data Division -- 4 Results -- 4.1 Segmentation Results -- 4.2 Axon Counts -- 5 Discussion -- 5.1 Limitations -- 6 Conclusion -- References -- Privileged Modality Guided Network for Retinal Vessel Segmentation in Ultra-Wide-Field Images -- 1 Introduction -- 2 Methodology -- 2.1 Overall Architecture -- 2.2 Consistency Regularization Loss -- 2.3 Vessel Extraction Network -- 3 Experiments and Results -- 3.1 Data Description -- 3.2 Implementation Details -- 3.3 Ablation Study -- 3.4 Comparisons with State-of-the-Art Methods -- 4 Conclusion -- References -- Adapting Segment Anything Model (SAM) for Retinal OCT -- 1 Introduction -- 2 Background and Related Works -- 3 Methods -- 4 Experimental Setup -- 4.1 Dataset and Evaluation -- 5 Results -- 6 Conclusion -- References -- Dual-Modality Grading of Keratoconus Severity Based on Corneal Topography and Clinical Indicators -- 1 Introduction -- 2 Method -- 2.1 Overall Architecture -- 2.2 Image Classifier on Corneal Topography -- 2.3 Data Classifier on Clinical Indicators -- 2.4 Fusion Strategy for Final Grading -- 3 Experiments and Results -- 3.1 Dataset. 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 3.4 Results and Discussion -- 4 Conclusion -- References -- Automated Optic Disc Finder and Segmentation Using Deep Learning for Blood Flow Studies in the Eye -- 1 Introduction -- 2 Methods -- 2.1 Data Preparation -- 2.2 Model Training -- 2.3 Model Evaluations -- 3 Results -- 4 Discussion and Conclusion -- References -- Multi-relational Graph Convolutional Neural Networks for Carotid Artery Stenosis Diagnosis via Fundus Images -- 1 Introduction -- 2 Methodology -- 2.1 Feature Extraction Layer -- 2.2 Constructing Multi-graphs -- 2.3 M-GCNs -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Results -- 4 Conclusion -- References -- Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT: A PINNACLE Study Report -- 1 Introduction -- 1.1 Related Work -- 2 Methods: Predictive Model from Retinal OCT Volume -- 3 Experiments -- 4 Results -- 5 Conclusion -- References -- A Structure-Consistency GAN for Unpaired AS-OCT Image Inpainting -- 1 Introduction -- 2 Proposed Method -- 2.1 Network Architecture -- 2.2 Objective Function -- 3 Experiments -- 4 Conclusion -- References -- STAGE Challenge: Structural-Functional Transition in Glaucoma Assessment Challenge in MICCAI 2023 -- 1 Introduction -- 2 Dataset -- 3 Baseline -- 4 Evaluation -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910746290603321 |
| Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Ophthalmic medical image analysis : 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings / / edited by Bhavna Antony [and five others]
| Ophthalmic medical image analysis : 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings / / edited by Bhavna Antony [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (215 pages) |
| Disciplina | 929.605 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Computer graphics |
| ISBN | 3-031-16525-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- AugPaste: One-Shot Anomaly Detection for Medical Images -- 1 Introduction -- 2 Methods -- 2.1 Construction of Lesion Bank -- 2.2 Synthesis of Anomalous Samples -- 2.3 Anomaly Detection Network -- 2.4 Implementation Details -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Evaluation Metric -- 3.3 Ablation Studies on EyeQ -- 3.4 Comparison with State-of-the-Art -- 4 Conclusion -- References -- Analysing Optical Coherence Tomography Angiography of Mid-Life Persons at Risk of Developing Alzheimer's Disease Later in Life -- 1 Introduction -- 2 Methodology -- 3 Results -- 3.1 Vessel Tortuosity Decreases in Risk Groups -- 3.2 Longitudinal Variations of Retinal Features in Risk Groups -- 4 Discussion -- 5 Conclusion -- References -- Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Robust Feature Learning Architecture -- 3.2 Proposed Representation Learning Loss -- 3.3 Final Objective Function -- 4 Experiments -- 4.1 Data-Set Processing -- 4.2 Hyper-parameter Tuning -- 4.3 Performance Metrics -- 4.4 Quantitative Evaluation -- 4.5 Qualitative Evaluation -- 5 Conclusion and Future Work -- References -- GUNet: A GCN-CNN Hybrid Model for Retinal Vessel Segmentation by Learning Graphical Structures -- 1 Introduction -- 2 Method -- 2.1 GUNet -- 2.2 Graph Convolution -- 2.3 Graph Construction -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Implementation Details -- 4 Results -- 4.1 Experiments on Fundus Photography -- 4.2 Experiments on SLO Images -- 4.3 Visualization -- 5 Conclusion -- References -- Detection of Diabetic Retinopathy Using Longitudinal -- 1 Introduction -- 2 Methods -- 2.1 Longitudinal Siamese -- 2.2 Longitudinal Self-supervised Learning.
2.3 Longitudinal Neighbourhood Embedding -- 3 Dataset -- 4 Experiments and Results -- 4.1 Comparison of the Approaches on the Early Change Detection -- 4.2 Norm of Trajectory Vector Analyze -- 5 Discussion -- References -- Multimodal Information Fusion for Glaucoma and Diabetic Retinopathy Classification -- 1 Introduction -- 2 Methods -- 2.1 Early Fusion -- 2.2 Intermediate Fusion -- 2.3 Hierarchical Fusion -- 3 Material and Experiments -- 3.1 Data -- 3.2 Data Pre-processing -- 3.3 Implementation Details -- 4 Results -- 4.1 GAMMA Dataset -- 4.2 PlexEliteDR Dataset -- 5 Conclusion -- References -- Mapping the Ocular Surface from Monocular Videos with an Application to Dry Eye Disease Grading -- 1 Introduction -- 2 Proposed Method -- 3 Experiments and Results -- 3.1 SiGMoid -- 3.2 DED Diagnosis: Classification -- 4 Discussion and Conclusion -- References -- Rethinking Retinal Image Quality: Treating Quality Threshold as a Tunable Hyperparameter -- 1 Introduction -- 2 Methods -- 2.1 Quality Prediction on a Categorical Scale and Continuous Scale -- 2.2 Effect of Varying Image Quality Threshold -- 3 Experiments -- 3.1 Altering Quality Threshold on a Categorical Scale -- 3.2 Altering Quality Threshold on a Continuous Scale -- 3.3 Tuning on a Continuous Scale: Does it Confer Additional Value? -- 4 Discussions and Conclusions -- References -- Robust and Efficient Computation of Retinal Fractal Dimension Through Deep Approximation -- 1 Introduction -- 2 Deep Approximation of Retinal Traits (DART) -- 2.1 Motivation and Theory -- 2.2 Implementation -- 3 Experiments -- 3.1 Data -- 3.2 Results -- 4 Conclusion -- References -- Localizing Anatomical Landmarks in Ocular Images Using Zoom-In Attentive Networks -- 1 Introduction -- 2 Method -- 2.1 Zoom-In Module -- 2.2 Attentive Fusion Module -- 3 Experiments and Results -- 3.1 Datasets and Settings. 3.2 Experimental Setup -- 3.3 Results and Discussion -- 4 Conclusions -- References -- Intra-operative OCT (iOCT) Super Resolution: A Two-Stage Methodology Leveraging High Quality Pre-operative OCT Scans -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Two-Stage Super-Resolution Approach -- 2.3 Implementation Details -- 2.4 Evaluation Metrics -- 3 Results -- 3.1 Evaluation on Real iOCT Images -- 3.2 Evaluation on Pseudo iOCT Images -- 4 Discussion and Conclusions -- References -- Domain Adaptive Retinal Vessel Segmentation Guided by High-frequency Component -- 1 Introduction -- 2 Methodology -- 2.1 Fourier Domain Adaptation -- 2.2 High-frequency Component Extraction Based on Gaussian Filtering -- 2.3 Multi-input Deep Vessel Segmentation Model -- 3 Experiments -- 3.1 Experiment Settings -- 3.2 Comparison and Ablation Study -- 4 Conclusion -- References -- Tiny-Lesion Segmentation in OCT via Multi-scale Wavelet Enhanced Transformer -- 1 Introduction -- 2 Method -- 2.1 Overall Architecture -- 2.2 The Encoder Path -- 2.3 The Adaptive Multi-scale Transformer Module -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Implementation Detail -- 3.3 Comparison Study -- 3.4 Wavelet Feature Representation Visualization -- 3.5 Ablation Study -- 4 Conclusion -- References -- Dataset and Evaluation Algorithm Design for GOALS Challenge -- 1 Introduction -- 2 Dataset -- 3 Baseline -- 4 Evaluation -- 4.1 Task 1: Layer Segmentation -- 4.2 Task 2: Glaucoma Classification -- 5 Conclusion -- References -- Self-supervised Learning for Anomaly Detection in Fundus Image -- 1 Introduction -- 2 Methodology -- 2.1 Illumination Information Change Augmentation -- 2.2 Reconstruction for Reflectance Image -- 2.3 Semi-hard Negative Mining Strategy -- 3 Experiments and Result -- 3.1 Dataset -- 3.2 Anomaly Score -- 3.3 Ablation Study. 3.4 Comparison with the State-of-the-Arts(SOTA) -- 3.5 Qualitative Analysis -- 4 Conclusion -- References -- GARDNet: Robust Multi-view Network for Glaucoma Classification in Color Fundus Images -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preprocessing -- 3.2 Multi-view Classification Network -- 4 Datasets -- 5 Experimental Setup -- 6 Experiments and Results -- 7 Discussion -- 8 Conclusion -- References -- Fundus Photograph Defect Repair Algorithm Based on Portable Camera Empty Shot -- 1 Introduction -- 2 Related Work -- 2.1 Image Enhancement -- 2.2 Image Inpainting -- 3 The Proposed Method -- 3.1 Camera Empty Shot Image -- 3.2 Compensation Template -- 4 Evaluations -- 4.1 Data Set and Experimental Setup -- 4.2 Experimental Results -- 5 Conclusions -- References -- Template Mask Based Image Fusion Built-in Algorithm for Wide Field Fundus Cameras -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Wide Field Fundus Image Pre-processing -- 3.2 Color and Brightness Normalization Based on Poisson Fusion -- 3.3 Image Fusion Based on Template Mask -- 3.4 Adaptive Brightness Adjustment -- 4 Evaluations -- 4.1 Data Set and Experimental Setup -- 4.2 Results and Analysis -- 5 Conclusions -- References -- Investigating the Vulnerability of Federated Learning-Based Diabetic Retinopathy Grade Classification to Gradient Inversion Attacks -- 1 Introduction -- 2 Methods and Materials -- 2.1 Data -- 2.2 Federated Learning and Gradient Inversion Attack Framework -- 2.3 Segmentation Matching Score -- 2.4 Evaluation -- 3 Results -- 3.1 Gradient Inversion Attack Performance -- 3.2 Extracting Identifiable Clinical Features from Reconstructed Images -- 4 Conclusions -- References -- Extraction of Eye Redness for Standardized Ocular Surface Photography -- 1 Introduction -- 2 Methods. 2.1 High-Resolution, Standardized Ocular Surface Photography System -- 2.2 Image Data Set -- 2.3 Automated Sclera Detection -- 2.4 Eye Redness Extraction -- 2.5 Ocular Surface Tile Annotation -- 3 Experiments and Results -- 3.1 Ocular Surface Tile Annotation -- 3.2 Automated Tile Classification -- 3.3 Redness Extraction -- 4 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996490363103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Ophthalmic medical image analysis : 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings / / edited by Bhavna Antony [and five others]
| Ophthalmic medical image analysis : 9th International Workshop, OMIA 2022, Held in Conjunction with MICCAI 2022, Singapore, Singapore, September 22, 2022, Proceedings / / edited by Bhavna Antony [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (215 pages) |
| Disciplina | 929.605 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Computer graphics |
| ISBN | 3-031-16525-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- AugPaste: One-Shot Anomaly Detection for Medical Images -- 1 Introduction -- 2 Methods -- 2.1 Construction of Lesion Bank -- 2.2 Synthesis of Anomalous Samples -- 2.3 Anomaly Detection Network -- 2.4 Implementation Details -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Evaluation Metric -- 3.3 Ablation Studies on EyeQ -- 3.4 Comparison with State-of-the-Art -- 4 Conclusion -- References -- Analysing Optical Coherence Tomography Angiography of Mid-Life Persons at Risk of Developing Alzheimer's Disease Later in Life -- 1 Introduction -- 2 Methodology -- 3 Results -- 3.1 Vessel Tortuosity Decreases in Risk Groups -- 3.2 Longitudinal Variations of Retinal Features in Risk Groups -- 4 Discussion -- 5 Conclusion -- References -- Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Robust Feature Learning Architecture -- 3.2 Proposed Representation Learning Loss -- 3.3 Final Objective Function -- 4 Experiments -- 4.1 Data-Set Processing -- 4.2 Hyper-parameter Tuning -- 4.3 Performance Metrics -- 4.4 Quantitative Evaluation -- 4.5 Qualitative Evaluation -- 5 Conclusion and Future Work -- References -- GUNet: A GCN-CNN Hybrid Model for Retinal Vessel Segmentation by Learning Graphical Structures -- 1 Introduction -- 2 Method -- 2.1 GUNet -- 2.2 Graph Convolution -- 2.3 Graph Construction -- 3 Experiments -- 3.1 Datasets and Evaluation Metrics -- 3.2 Implementation Details -- 4 Results -- 4.1 Experiments on Fundus Photography -- 4.2 Experiments on SLO Images -- 4.3 Visualization -- 5 Conclusion -- References -- Detection of Diabetic Retinopathy Using Longitudinal -- 1 Introduction -- 2 Methods -- 2.1 Longitudinal Siamese -- 2.2 Longitudinal Self-supervised Learning.
2.3 Longitudinal Neighbourhood Embedding -- 3 Dataset -- 4 Experiments and Results -- 4.1 Comparison of the Approaches on the Early Change Detection -- 4.2 Norm of Trajectory Vector Analyze -- 5 Discussion -- References -- Multimodal Information Fusion for Glaucoma and Diabetic Retinopathy Classification -- 1 Introduction -- 2 Methods -- 2.1 Early Fusion -- 2.2 Intermediate Fusion -- 2.3 Hierarchical Fusion -- 3 Material and Experiments -- 3.1 Data -- 3.2 Data Pre-processing -- 3.3 Implementation Details -- 4 Results -- 4.1 GAMMA Dataset -- 4.2 PlexEliteDR Dataset -- 5 Conclusion -- References -- Mapping the Ocular Surface from Monocular Videos with an Application to Dry Eye Disease Grading -- 1 Introduction -- 2 Proposed Method -- 3 Experiments and Results -- 3.1 SiGMoid -- 3.2 DED Diagnosis: Classification -- 4 Discussion and Conclusion -- References -- Rethinking Retinal Image Quality: Treating Quality Threshold as a Tunable Hyperparameter -- 1 Introduction -- 2 Methods -- 2.1 Quality Prediction on a Categorical Scale and Continuous Scale -- 2.2 Effect of Varying Image Quality Threshold -- 3 Experiments -- 3.1 Altering Quality Threshold on a Categorical Scale -- 3.2 Altering Quality Threshold on a Continuous Scale -- 3.3 Tuning on a Continuous Scale: Does it Confer Additional Value? -- 4 Discussions and Conclusions -- References -- Robust and Efficient Computation of Retinal Fractal Dimension Through Deep Approximation -- 1 Introduction -- 2 Deep Approximation of Retinal Traits (DART) -- 2.1 Motivation and Theory -- 2.2 Implementation -- 3 Experiments -- 3.1 Data -- 3.2 Results -- 4 Conclusion -- References -- Localizing Anatomical Landmarks in Ocular Images Using Zoom-In Attentive Networks -- 1 Introduction -- 2 Method -- 2.1 Zoom-In Module -- 2.2 Attentive Fusion Module -- 3 Experiments and Results -- 3.1 Datasets and Settings. 3.2 Experimental Setup -- 3.3 Results and Discussion -- 4 Conclusions -- References -- Intra-operative OCT (iOCT) Super Resolution: A Two-Stage Methodology Leveraging High Quality Pre-operative OCT Scans -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Two-Stage Super-Resolution Approach -- 2.3 Implementation Details -- 2.4 Evaluation Metrics -- 3 Results -- 3.1 Evaluation on Real iOCT Images -- 3.2 Evaluation on Pseudo iOCT Images -- 4 Discussion and Conclusions -- References -- Domain Adaptive Retinal Vessel Segmentation Guided by High-frequency Component -- 1 Introduction -- 2 Methodology -- 2.1 Fourier Domain Adaptation -- 2.2 High-frequency Component Extraction Based on Gaussian Filtering -- 2.3 Multi-input Deep Vessel Segmentation Model -- 3 Experiments -- 3.1 Experiment Settings -- 3.2 Comparison and Ablation Study -- 4 Conclusion -- References -- Tiny-Lesion Segmentation in OCT via Multi-scale Wavelet Enhanced Transformer -- 1 Introduction -- 2 Method -- 2.1 Overall Architecture -- 2.2 The Encoder Path -- 2.3 The Adaptive Multi-scale Transformer Module -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Implementation Detail -- 3.3 Comparison Study -- 3.4 Wavelet Feature Representation Visualization -- 3.5 Ablation Study -- 4 Conclusion -- References -- Dataset and Evaluation Algorithm Design for GOALS Challenge -- 1 Introduction -- 2 Dataset -- 3 Baseline -- 4 Evaluation -- 4.1 Task 1: Layer Segmentation -- 4.2 Task 2: Glaucoma Classification -- 5 Conclusion -- References -- Self-supervised Learning for Anomaly Detection in Fundus Image -- 1 Introduction -- 2 Methodology -- 2.1 Illumination Information Change Augmentation -- 2.2 Reconstruction for Reflectance Image -- 2.3 Semi-hard Negative Mining Strategy -- 3 Experiments and Result -- 3.1 Dataset -- 3.2 Anomaly Score -- 3.3 Ablation Study. 3.4 Comparison with the State-of-the-Arts(SOTA) -- 3.5 Qualitative Analysis -- 4 Conclusion -- References -- GARDNet: Robust Multi-view Network for Glaucoma Classification in Color Fundus Images -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preprocessing -- 3.2 Multi-view Classification Network -- 4 Datasets -- 5 Experimental Setup -- 6 Experiments and Results -- 7 Discussion -- 8 Conclusion -- References -- Fundus Photograph Defect Repair Algorithm Based on Portable Camera Empty Shot -- 1 Introduction -- 2 Related Work -- 2.1 Image Enhancement -- 2.2 Image Inpainting -- 3 The Proposed Method -- 3.1 Camera Empty Shot Image -- 3.2 Compensation Template -- 4 Evaluations -- 4.1 Data Set and Experimental Setup -- 4.2 Experimental Results -- 5 Conclusions -- References -- Template Mask Based Image Fusion Built-in Algorithm for Wide Field Fundus Cameras -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Wide Field Fundus Image Pre-processing -- 3.2 Color and Brightness Normalization Based on Poisson Fusion -- 3.3 Image Fusion Based on Template Mask -- 3.4 Adaptive Brightness Adjustment -- 4 Evaluations -- 4.1 Data Set and Experimental Setup -- 4.2 Results and Analysis -- 5 Conclusions -- References -- Investigating the Vulnerability of Federated Learning-Based Diabetic Retinopathy Grade Classification to Gradient Inversion Attacks -- 1 Introduction -- 2 Methods and Materials -- 2.1 Data -- 2.2 Federated Learning and Gradient Inversion Attack Framework -- 2.3 Segmentation Matching Score -- 2.4 Evaluation -- 3 Results -- 3.1 Gradient Inversion Attack Performance -- 3.2 Extracting Identifiable Clinical Features from Reconstructed Images -- 4 Conclusions -- References -- Extraction of Eye Redness for Standardized Ocular Surface Photography -- 1 Introduction -- 2 Methods. 2.1 High-Resolution, Standardized Ocular Surface Photography System -- 2.2 Image Data Set -- 2.3 Automated Sclera Detection -- 2.4 Eye Redness Extraction -- 2.5 Ocular Surface Tile Annotation -- 3 Experiments and Results -- 3.1 Ocular Surface Tile Annotation -- 3.2 Automated Tile Classification -- 3.3 Redness Extraction -- 4 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910595062203321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||