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Advances in Computer Graphics [[electronic resource] ] : 40th Computer Graphics International Conference, CGI 2023, Shanghai, China, August 28 – September 1, 2023, Proceedings, Part III / / edited by Bin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann
| Advances in Computer Graphics [[electronic resource] ] : 40th Computer Graphics International Conference, CGI 2023, Shanghai, China, August 28 – September 1, 2023, Proceedings, Part III / / edited by Bin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann |
| Autore | Sheng Bin |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (522 pages) |
| Disciplina | 005.3 |
| Altri autori (Persone) |
BiLei
KimJinman Magnenat-ThalmannNadia ThalmannDaniel |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Application software
Computer systems Computer networks Data structures (Computer science) Information theory Coding theory Computer science Computer and Information Systems Applications Computer System Implementation Computer Communication Networks Data Structures and Information Theory Coding and Information Theory Theory of Computation |
| ISBN | 3-031-50075-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Detection and Recognition -- Image Analysis and Processing; Image Restoration and Enhancement; Image Attention and Perception -- Reconstruction; Rendering and Animation -- Synthesis and Generation -- Visual Analytics and Modeling; Graphics and AR/VR -- Medical Imaging and Robotics -- Theoretical Analysis; Image Analysis and Visualization in Advanced Medical Imaging Technology -- Empowering Novel Geometric Algebra for Graphics and Engineering. |
| Record Nr. | UNINA-9910805582603321 |
Sheng Bin
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in Computer Graphics [[electronic resource] ] : 40th Computer Graphics International Conference, CGI 2023, Shanghai, China, August 28 – September 1, 2023, Proceedings, Part I / / edited by Bin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann
| Advances in Computer Graphics [[electronic resource] ] : 40th Computer Graphics International Conference, CGI 2023, Shanghai, China, August 28 – September 1, 2023, Proceedings, Part I / / edited by Bin Sheng, Lei Bi, Jinman Kim, Nadia Magnenat-Thalmann, Daniel Thalmann |
| Autore | Sheng Bin |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (509 pages) |
| Disciplina | 005.3 |
| Altri autori (Persone) |
BiLei
KimJinman Magnenat-ThalmannNadia ThalmannDaniel |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Application software
Computer systems Computer networks Data structures (Computer science) Information theory Coding theory Computer science Computer and Information Systems Applications Computer System Implementation Computer Communication Networks Data Structures and Information Theory Coding and Information Theory Theory of Computation |
| ISBN | 3-031-50069-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Detection and Recognition -- Image Analysis and Processing; Image Restoration and Enhancement; Image Attention and Perception -- Reconstruction; Rendering and Animation -- Synthesis and Generation -- Visual Analytics and Modeling; Graphics and AR/VR -- Medical Imaging and Robotics -- Theoretical Analysis; Image Analysis and Visualization in Advanced Medical Imaging Technology -- Empowering Novel Geometric Algebra for Graphics and Engineering. |
| Record Nr. | UNINA-9910805575303321 |
|
Sheng Bin
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mitosis Domain Generalization and Diabetic Retinopathy Analysis [[electronic resource] ] : MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings / / edited by Bin Sheng, Marc Aubreville
| Mitosis Domain Generalization and Diabetic Retinopathy Analysis [[electronic resource] ] : MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings / / edited by Bin Sheng, Marc Aubreville |
| Autore | Sheng Bin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (250 pages) |
| Disciplina | 006 |
| Altri autori (Persone) | AubrevilleMarc |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing—Digital techniques
Computer vision Computers Application software Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Computing Milieux Computer and Information Systems Applications Machine Learning |
| Soggetto non controllato |
Ophthalmology
Medical |
| ISBN | 3-031-33658-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface DRAC 2022 -- nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy Analysis -- Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias -- Bag of Tricks for Diabetic Retinopathy Grading of Ultra-wide Optical Coherence Tomography Angiography Images -- Deep convolutional neural network for image quality assessment and diabetic retinopathy grading -- Diabetic Retinal Overlap Lesion Segmentation Network -- An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images -- Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity -- Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images -- Deep Learning-based Multi-tasking System for Diabetic Retinopathy in UW-OCTA images -- Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment -- Image Quality Assessment based on Multi-Model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA Images -- An improved U-Net for diabetic retinopathy segmentation -- A Vision transformer based deep learning architecture for automatic diagnosis of diabetic retinopathy in optical coherence tomography angiography -- Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy -- Data Augmentation by Fourier Transformation for Class-Imbalance : Application to Medical Image Quality Assessment -- Automatic image quality assessment and DR grading method based on convolutional neural network -- A transfer learning based model ensemble method for image quality assessment and diabetic retinopathy grading -- Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images using Transfer Learning -- Preface MIDOG 2022 -- Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge -- Radial Prediction Domain Adaption Classifier for the MIDOG 2022 challenge -- Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge -- Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization -- "A Deep Learning based Ensemble Model for Generalized Mitosis Detection in H&E stained Whole Slide Images" -- Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset -- Multi-task RetinaNet for mitosis detection. . |
| Record Nr. | UNISA-996534463903316 |
|
Sheng Bin
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Mitosis Domain Generalization and Diabetic Retinopathy Analysis : MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings / / edited by Bin Sheng, Marc Aubreville
| Mitosis Domain Generalization and Diabetic Retinopathy Analysis : MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings / / edited by Bin Sheng, Marc Aubreville |
| Autore | Sheng Bin |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (250 pages) |
| Disciplina |
006
617.735 |
| Altri autori (Persone) | AubrevilleMarc |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Image processing—Digital techniques
Computer vision Computers Application software Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Computing Milieux Computer and Information Systems Applications Machine Learning |
| Soggetto non controllato |
Ophthalmology
Medical |
| ISBN | 3-031-33658-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface DRAC 2022 -- nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy Analysis -- Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias -- Bag of Tricks for Diabetic Retinopathy Grading of Ultra-wide Optical Coherence Tomography Angiography Images -- Deep convolutional neural network for image quality assessment and diabetic retinopathy grading -- Diabetic Retinal Overlap Lesion Segmentation Network -- An Ensemble Method to Automatically Grade Diabetic Retinopathy with Optical Coherence Tomography Angiography Images -- Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity -- Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images -- Deep Learning-based Multi-tasking System for Diabetic Retinopathy in UW-OCTA images -- Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment -- Image Quality Assessment based on Multi-Model Ensemble Class-Imbalance Repair Algorithm for Diabetic Retinopathy UW-OCTA Images -- An improved U-Net for diabetic retinopathy segmentation -- A Vision transformer based deep learning architecture for automatic diagnosis of diabetic retinopathy in optical coherence tomography angiography -- Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy -- Data Augmentation by Fourier Transformation for Class-Imbalance : Application to Medical Image Quality Assessment -- Automatic image quality assessment and DR grading method based on convolutional neural network -- A transfer learning based model ensemble method for image quality assessment and diabetic retinopathy grading -- Automatic Diabetic Retinopathy Lesion Segmentation in UW-OCTA Images using Transfer Learning -- Preface MIDOG 2022 -- Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge -- Radial Prediction Domain Adaption Classifier for the MIDOG 2022 challenge -- Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge -- Tackling Mitosis Domain Generalization in Histopathology Images with Color Normalization -- "A Deep Learning based Ensemble Model for Generalized Mitosis Detection in H&E stained Whole Slide Images" -- Fine-Grained Hard-Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset -- Multi-task RetinaNet for mitosis detection. . |
| Record Nr. | UNINA-9910728397403321 |
|
Sheng Bin
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Myopic Maculopathy Analysis : MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8-12, 2023, Proceedings
| Myopic Maculopathy Analysis : MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8-12, 2023, Proceedings |
| Autore | Sheng Bin |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (131 pages) |
| Altri autori (Persone) |
ChenHao
WongTien Yin |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-54857-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Task 1: Classification of Myopic Maculopathy -- 2.3 Task 2: Segmentation of Myopic Maculopathy Plus Lesions -- 2.4 Task 3: Prediction of Spherical Equivalent -- 2.5 Implementation Details -- 3 Results -- 3.1 Task 1: Classification of Myopic Maculopathy -- 3.2 Task 2: Segmentation of Myopic Maculopathy Plus Lesions -- 3.3 Task 3: Prediction of Spherical Equivalent -- 4 Discussion and Conclusions -- References -- Swin-MMC: Swin-Based Model for Myopic Maculopathy Classification in Fundus Images -- 1 Introduction -- 2 Method -- 2.1 Enhanced ArcFace Loss with 3 Sub-centers -- 2.2 Weak Label -- 3 Experiments -- 3.1 Dataset and Evaluation Measures -- 3.2 Image Preprocessing and Augmentation -- 3.3 Implementation Details -- 4 Results and Discussion -- 4.1 Results on Testing Set -- 4.2 Visualization Heatmap Analysis -- 4.3 Ablation Study in Further Test Phase -- 4.4 Limitation and Future Work -- 5 Conclusion -- References -- Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Pre-training -- 3.2 Network Architecture -- 3.3 Pseudo Labeling -- 3.4 Image Resolution -- 3.5 Loss Function -- 3.6 Model Ensemble -- 4 Experiment -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Results on the Validation Set -- 4.5 Results on the MMAC Leaderboard -- 5 Conclusion -- References -- Ensemble Deep Learning Approaches for Myopic Maculopathy Plus Lesions Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Network Architecture -- 3.2 Loss Function -- 3.3 Model Ensemble -- 4 Experiments.
4.1 Dataset -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Results on the Validation Set -- 4.5 Results on the Leaderboard -- 4.6 Visual Segmentation Results -- 5 Conclusion -- References -- Beyond MobileNet: An Improved MobileNet for Retinal Diseases -- 1 Introduction -- 2 Related Work -- 2.1 CNN-Based Method for RD Diagnosis -- 2.2 VIT-Based Method for RD Diagnosis -- 3 Methods -- 3.1 Network Design -- 3.2 Training Techniques -- 4 MMAC - Classification of Myopic Maculopathy -- 4.1 Dataset and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 5 Conclusion -- References -- Prediction of Spherical Equivalent with Vanilla ResNet -- 1 Introduction -- 2 Related Works -- 3 Methods -- 4 Results -- 5 Discussion: The Significance of Proper Data Augmentation -- 6 Conclusion -- References -- Semi-supervised Learning for Myopic Maculopathy Analysis -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 4 Segmentation of Myopic Maculopathy Plus Lesions -- 5 Prediction of Spherical Equivalent -- 6 Conclusions -- References -- A Clinically Guided Approach for Training Deep Neural Networks for Myopic Maculopathy Classification -- 1 Introduction -- 2 Methods -- 2.1 Datasets and Pre-processing -- 2.2 Image Synthesis Pipeline Guided by Clinical Domain Knowledge -- 2.3 Mix-Up Augmentation -- 2.4 Evaluation Metrics -- 2.5 Training Details -- 2.6 Ensemble Prediction via Test-Time Augmentation -- 3 Results -- 4 Conclusions and Future Directions -- References -- Classification of Myopic Maculopathy Images with Self-supervised Driven Multiple Instance Learning Network -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning in Myopic Maculopathy Analysis -- 2.2 Multiple Instance Learning -- 2.3 Self-supervised Learning -- 3 Methodology -- 3.1 Generative Data Augmentation -- 3.2 Backbone Architecture -- 4 Experiments. 4.1 Datasets and Implementation -- 4.2 Results -- 5 Conclusion -- References -- Self-supervised Learning and Data Diversity Based Prediction of Spherical Equivalent -- 1 Introduction -- 2 Our Solution -- 2.1 Baseline -- 2.2 Self-supervised Learning -- 2.3 Increase Data Diversity -- 2.4 Part of Data -- 2.5 Test-Time Augmentation -- 3 Experiment -- 3.1 Implement Details -- 3.2 Experiment Results -- 4 Conclusion -- References -- Myopic Maculopathy Analysis Using Multi-task Learning and Pseudo Labeling -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Multi-task Learning -- 3.2 Pseudo-labeling -- 4 Results -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNINA-9910842287103321 |
|
Sheng Bin
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Myopic Maculopathy Analysis : MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8-12, 2023, Proceedings
| Myopic Maculopathy Analysis : MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8-12, 2023, Proceedings |
| Autore | Sheng Bin |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (131 pages) |
| Altri autori (Persone) |
ChenHao
WongTien Yin |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-54857-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Task 1: Classification of Myopic Maculopathy -- 2.3 Task 2: Segmentation of Myopic Maculopathy Plus Lesions -- 2.4 Task 3: Prediction of Spherical Equivalent -- 2.5 Implementation Details -- 3 Results -- 3.1 Task 1: Classification of Myopic Maculopathy -- 3.2 Task 2: Segmentation of Myopic Maculopathy Plus Lesions -- 3.3 Task 3: Prediction of Spherical Equivalent -- 4 Discussion and Conclusions -- References -- Swin-MMC: Swin-Based Model for Myopic Maculopathy Classification in Fundus Images -- 1 Introduction -- 2 Method -- 2.1 Enhanced ArcFace Loss with 3 Sub-centers -- 2.2 Weak Label -- 3 Experiments -- 3.1 Dataset and Evaluation Measures -- 3.2 Image Preprocessing and Augmentation -- 3.3 Implementation Details -- 4 Results and Discussion -- 4.1 Results on Testing Set -- 4.2 Visualization Heatmap Analysis -- 4.3 Ablation Study in Further Test Phase -- 4.4 Limitation and Future Work -- 5 Conclusion -- References -- Towards Label-Efficient Deep Learning for Myopic Maculopathy Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Pre-training -- 3.2 Network Architecture -- 3.3 Pseudo Labeling -- 3.4 Image Resolution -- 3.5 Loss Function -- 3.6 Model Ensemble -- 4 Experiment -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Results on the Validation Set -- 4.5 Results on the MMAC Leaderboard -- 5 Conclusion -- References -- Ensemble Deep Learning Approaches for Myopic Maculopathy Plus Lesions Segmentation -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Network Architecture -- 3.2 Loss Function -- 3.3 Model Ensemble -- 4 Experiments.
4.1 Dataset -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Results on the Validation Set -- 4.5 Results on the Leaderboard -- 4.6 Visual Segmentation Results -- 5 Conclusion -- References -- Beyond MobileNet: An Improved MobileNet for Retinal Diseases -- 1 Introduction -- 2 Related Work -- 2.1 CNN-Based Method for RD Diagnosis -- 2.2 VIT-Based Method for RD Diagnosis -- 3 Methods -- 3.1 Network Design -- 3.2 Training Techniques -- 4 MMAC - Classification of Myopic Maculopathy -- 4.1 Dataset and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Experimental Results -- 5 Conclusion -- References -- Prediction of Spherical Equivalent with Vanilla ResNet -- 1 Introduction -- 2 Related Works -- 3 Methods -- 4 Results -- 5 Discussion: The Significance of Proper Data Augmentation -- 6 Conclusion -- References -- Semi-supervised Learning for Myopic Maculopathy Analysis -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 4 Segmentation of Myopic Maculopathy Plus Lesions -- 5 Prediction of Spherical Equivalent -- 6 Conclusions -- References -- A Clinically Guided Approach for Training Deep Neural Networks for Myopic Maculopathy Classification -- 1 Introduction -- 2 Methods -- 2.1 Datasets and Pre-processing -- 2.2 Image Synthesis Pipeline Guided by Clinical Domain Knowledge -- 2.3 Mix-Up Augmentation -- 2.4 Evaluation Metrics -- 2.5 Training Details -- 2.6 Ensemble Prediction via Test-Time Augmentation -- 3 Results -- 4 Conclusions and Future Directions -- References -- Classification of Myopic Maculopathy Images with Self-supervised Driven Multiple Instance Learning Network -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning in Myopic Maculopathy Analysis -- 2.2 Multiple Instance Learning -- 2.3 Self-supervised Learning -- 3 Methodology -- 3.1 Generative Data Augmentation -- 3.2 Backbone Architecture -- 4 Experiments. 4.1 Datasets and Implementation -- 4.2 Results -- 5 Conclusion -- References -- Self-supervised Learning and Data Diversity Based Prediction of Spherical Equivalent -- 1 Introduction -- 2 Our Solution -- 2.1 Baseline -- 2.2 Self-supervised Learning -- 2.3 Increase Data Diversity -- 2.4 Part of Data -- 2.5 Test-Time Augmentation -- 3 Experiment -- 3.1 Implement Details -- 3.2 Experiment Results -- 4 Conclusion -- References -- Myopic Maculopathy Analysis Using Multi-task Learning and Pseudo Labeling -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Multi-task Learning -- 3.2 Pseudo-labeling -- 4 Results -- 5 Conclusion -- References -- Author Index. |
| Record Nr. | UNISA-996587859203316 |
|
Sheng Bin
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||