ListarGI-VARPA - Artigos por tema "Deep learning"
Mostrando ítems 1-20 de 30
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Adapted generative latent diffusion models for accurate pathological analysis in chest X-ray images
(Springer Nature, 2024-03-19)[Absctract]: Respiratory diseases have a significant global impact, and assessing these conditions is crucial for improving patient outcomes. Chest X-ray is widely used for diagnosis, but expert evaluation can be challenging. ... -
Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation
(Optica Publishing Group, 2023-07)[Abstract]: Optical coherence tomography (OCT) is the most widely used imaging modality in ophthalmology. There are multiple variations of OCT imaging capable of producing complementary information. Thus, registering these ... -
Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios
(IEEE, 2023-11)[Absctract]: Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful ... -
Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches
(Elsevier, 2024-04)[Abstract]: Recent clinical studies have emphasized the importance of understanding the morphology and mechanics of the ciliary muscle. The ciliary muscle plays a vital role in various functions related to the anterior ... -
Color Fundus Image Registration Using a Learning-Based Domain-Specific Landmark Detection Methodology
(Elsevier, 2022)[Abstract] Medical imaging, and particularly retinal imaging, allows to accurately diagnose many eye pathologies as well as some systemic diseases such as hypertension or diabetes. Registering these images is crucial to ... -
Comparative study of the glistening between four intraocular lens models assessed by OCT and deep learning
(Wolters Kluwer on behalf of ASCRS and ESCRS, 2024-01)Purpose: To evaluate the glistening in 4 different models of intraocular lenses (IOLs) using optical coherence tomography (OCT) and deep learning (DL). Setting: Centro Internacional de Oftalmología Avanzada (Madrid, ... -
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 ... -
Data Augmentation Approaches Using Cycle-Consistent Adversarial Networks for Improving COVID-19 Screening in Portable Chest X-Ray Images
(Elsevier, 2021)[Abstract] The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of ... -
Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices
(Institute of Electrical and Electronics Engineers, 2020-10-26)[Abstract] The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest ... -
Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images
(Elsevier, 2020-04)[Abstract]: Background and objectives:The analysis of the retinal vasculature plays an important role in the diagnosis of many ocular and systemic diseases. In this context, the accurate detection of the vessel crossings ... -
Deformable registration of multimodal retinal images using a weakly supervised deep learning approach
(Springer, 2023-03-03)[Absctract]: There are different retinal vascular imaging modalities widely used in clinical practice to diagnose different retinal pathologies. The joint analysis of these multimodal images is of increasing interest since ... -
Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?
(BMC, 2022)[Abstract] Background The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study ... -
End-To-End Multi-Task Learning Approaches for the Joint Epiretinal Membrane Segmentation and Screening in OCT Images
(Elsevier, 2022)[Abstract] Background and objectives The Epiretinal Membrane (ERM) is an ocular disease that can cause visual distortions and irreversible vision loss. Patient sight preservation relies on an early diagnosis and on determining ... -
End-To-End Multi-Task Learning for Simultaneous Optic Disc and Cup Segmentation and Glaucoma Classification in Eye Fundus Images
(Elsevier, 2022)[Abstract] The automated analysis of eye fundus images is crucial towards facilitating the screening and early diagnosis of glaucoma. Nowadays, there are two common alternatives for the diagnosis of this disease using deep ... -
Explainable artificial intelligence for the automated assessment of the retinal vascular tortuosity
(Springer, 2024)[Abstract]: Retinal vascular tortuosity is an excessive bending and twisting of the blood vessels in the retina that is associated with numerous health conditions. We propose a novel methodology for the automated assessment ... -
Fully Automatic Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images
(Elsevier, 2022)[Abstract] Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as ... -
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 ... -
Joint keypoint detection and description network for color fundus image registration
(AME Publishing Company, 2023-07-01)[Absctract]: Background: Retinal imaging is widely used to diagnose many diseases, both systemic and eye-specific. In these cases, image registration, which is the process of aligning images taken from different viewpoints ... -
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 ...