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.
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. | UNISA-996587859203316 |
|
Sheng Bin
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||