Browsing Grupo de Visión Artificial e Recoñecemento de Patróns (VARPA) by Subject "Deep learning"
Now showing items 1-20 of 47
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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. ... -
Analysis of Imbalanced Datasets in the Performance of Deep Learning Approaches for COVID-19 Screening from Chest X-ray Imaging: Impact of Sex and Age Factors
(EasyChair, 2023-02-16)[Absctract]: In this work, we analysed 11 imbalance scenarios with female and male COVID-19 patients present in different proportions for the sex analysis, and 6 scenarios where only one specific age range was used for ... -
Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks
(MDPI AG, 2020-08-21)[Abstract] The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the ... -
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 ... -
Choroid segmentation in non-EDI OCT images of multiple sclerosis patients
(A. Leitao and L. Ramos (eds.), 2023)[Abstract]: Optical coherence tomography (OCT) is a non-invasive diagnostic technique that can image ocular structures. Recently, this imaging technique has been used to diagnose and monitor patients with multiple sclerosis ... -
Clinical Decision Support Tool for the Identification of Pathological Structures Associated with Age-Related Macular Degeneration
(Springer Science and Business Media Deutschland GmbH, 2022)[Abstract]: In the field of ophthalmology, different imaging modalities are commonly used to carry out different clinical diagnostic procedures. Currently, both optical coherence tomography (OCT) and optical coherence ... -
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, ... -
Computational Radiological Screening of Patients with COVID-19 Using Chest X-ray Images from Portable Devices
(MDPI, 2021)[Abstract] This work presents a fully automatic system for the screening of chest X-ray images from portable devices under the analysis of three different clinical categories: normal, pathological cases of pulmonary diseases ... -
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 ... -
Cycle generative adversarial network approaches to produce novel portable chest X-rays images for covid-19 diagnosis
(Institute of Electrical and Electronics Engineers Inc., 2021)[Abstract]: Coronavirus Disease 2019 (COVID-19), declared a global pandemic by the World Health Organization, mainly affects the pulmonary tissues, playing chest X-ray images an important role for its screening and early ... -
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 ... -
Data Extraction in Insurance Photo-Inspections Using Computer Vision
(MDPI AG, 2020-08-21)[Abstract] Recent advances in computer vision and artificial intelligence allow for a better processing of complex information in many fields of human activity. One such field is vehicle expertise and inspection. This ... -
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 ... -
Deep Multi-Segmentation Approach for the Joint Classification and Segmentation of the Retinal Arterial and Venous Trees in Color Fundus Images
(MDPI, 2021)[Abstract] The analysis of the retinal vasculature represents a crucial stage in the diagnosis of several diseases. An exhaustive analysis involves segmenting the retinal vessels and classifying them into veins and arteries. ... -
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 ...