Listar GI-VARPA - Artigos por título
Mostrando ítems 41-60 de 60
-
Image-to-image translation with Generative Adversarial Networks via retinal masks for realistic Optical Coherence Tomography imaging of Diabetic Macular Edema disorders
(Elsevier, 2023)[Abstract]: One of the main issues with deep learning is the need of a significant number of samples. We intend to address this problem in the field of Optical Coherence Tomography (OCT), specifically in the context of ... -
Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images
(Springer, 2024-02)[Absctract]: Central Serous Chorioretinopathy (CSC) is a retinal disorder caused by the accumulation of fluid, resulting in vision distortion. The diagnosis of this disease is typically performed through Optical Coherence ... -
Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images
(MDPI AG, 2020-04-03)[Abstract] Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is ... -
Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images
(Springer, 2020-06-19)[Abstract]: The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the ... -
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 ... -
Multi-Adaptive Optimization for multi-task learning with deep neural networks
(Elsevier B.V., 2024-02)[Abstract]: Multi-task learning is a promising paradigm to leverage task interrelations during the training of deep neural networks. A key challenge in the training of multi-task networks is to adequately balance the ... -
Multi-Stage Transfer Learning for Lung Segmentation Using Portable X-Ray Devices for Patients With COVID-19
(Elsevier BV, 2021-07)[Abstract] One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the ... -
Multi-task localization of the hemidiaphragms and lung segmentation in portable chest X-ray images of COVID-19 patients
(Sage, 2024-02-01)[Absctract]: Background: The COVID-19 can cause long-term symptoms in the patients after they overcome the disease. Given that this disease mainly damages the respiratory system, these symptoms are often related with ... -
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, ... -
Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images
(Springer, 2023-01-24)[Abstract]: Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema ... -
On Strongly Inflexible Manifolds
(Oxford University Press, 2023-05)[Abstract]: An oriented closed connected -manifold is inflexible if it does not admit self-maps of unbounded degree. In addition, if all the maps from any other oriented closed connected -manifold have bounded degree, then ... -
Repeatability of choriocapillaris flow voids by optical coherence tomography angiography in central serous chorioretinopathy
(Public Library of Science, 2022-12-16)[Abstract]: Purpose: To assess the repeatability of flow signal voids (FSV) measurements of the choriocapillaris (CC) and choroid (CH) in central serous chorioretinopathy (CSCR) by Swept-Source optical coherence tomography ... -
Retinal Microaneurysms Detection Using Adversarial Pre-training With Unlabeled Multimodal Images
(Elsevier, 2022)[Abstract] The detection of retinal microaneurysms is crucial for the early detection of important diseases such as diabetic retinopathy. However, the detection of these lesions in retinography, the most widely available ... -
Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning
(AME Publishing, 2023-05-01)[Absctract]: Background: Glaucoma is the leading global cause of irreversible blindness. Glaucoma patients experience a progressive deterioration of the retinal nervous tissues that begins with a loss of peripheral vision. ... -
Robust multimodal registration of fluorescein angiography and optical coherence tomography angiography images using evolutionary algorithms
(2021)[Abstract] Optical coherence tomography angiography (OCTA) and fluorescein angiography (FA) are two different vascular imaging modalities widely used in clinical practice to diagnose and grade different relevant retinal ... -
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 ... -
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, ... -
Simultaneous Segmentation and Classification of the Retinal Arteries and Veins From Color Fundus Images
(Elsevier, 2021)[Abstract] Background and objectives: The study of the retinal vasculature represents a fundamental stage in the screening and diagnosis of many high-incidence diseases, both systemic and ophthalmic. A complete retinal ... -
Unsupervised contrastive unpaired image generation approach for improving tuberculosis screening using chest X-ray images
(Elsevier, 2022-12)[Abstract]: Tuberculosis is an infectious disease that mainly affects the lung tissues. Therefore, chest X-ray imaging can be very useful to diagnose and to understand the evolution of the pathology. This image modality ... -
Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning
(Elsevier Ireland Ltd, 2023-02)[Abstract]: Background and Objectives: Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness ...