Buscar
Mostrando ítems 1-5 de 5
Self-Supervised Multimodal Reconstruction Pre-training for Retinal Computer-Aided Diagnosis
(Elsevier, 2021)
[Abstract] Computer-aided diagnosis using retinal fundus images is crucial for the early detection of many ocular and systemic diseases. Nowadays, deep learning-based approaches are commonly used for this purpose. However, ...
Self-Supervised Multimodal Reconstruction of Retinal Images Over Paired Datasets
(Elsevier Ltd, 2020-12-15)
[Abstract]
Data scarcity represents an important constraint for the training of deep neural networks in medical imaging. Medical image labeling, especially if pixel-level annotations are required, is an expensive task ...
Learning the Retinal Anatomy From Scarce Annotated Data Using Self-Supervised Multimodal Reconstruction
(Elsevier BV, 2020-03-13)
[Abstract] Deep learning is becoming the reference paradigm for approaching many computer vision problems. Nevertheless, the training of deep neural networks typically requires a significantly large amount of annotated ...
Context encoder transfer learning approaches for retinal image analysis
(Elsevier Ltd, 2023-01)
[Abstract]: During the last years, deep learning techniques have emerged as powerful alternatives to solve biomedical image analysis problems. However, the training of deep neural networks usually needs great amounts of ...
Multimodal Image Encoding Pre-training for Diabetic Retinopathy Grading
(Elsevier, 2022)
[Abstract] Diabetic retinopathy is an increasingly prevalent eye disorder that can lead to severe vision impairment. The severity grading of the disease using retinal images is key to provide an adequate treatment. However, ...