Myopic Maculopathy Analysis : MICCAI Challenge MMAC 2023, Held in Conjunction with MICCAI 2023, Virtual Event, October 8-12, 2023, Proceedings

• Autore: Sheng Bin
• Pubblicazione: Cham : , : Springer, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (131 pages)
• Altri autori: ChenHao WongTien Yin
• 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.
• Titolo autorizzato: Myopic Maculopathy Analysis
• ISBN: 3-031-54857-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996587859203316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science Series