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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 ...
Portable Chest X-ray Synthetic Image Generation for the COVID-19 Screening
(MDPI, 2021)
[Abstract] The global pandemic of COVID-19 raises the importance of having fast and reliable methods to perform an early detection and to visualize the evolution of the disease in every patient, which can be assessed with ...
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 ...
Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models
(Elsevier, 2023-07)
[Abstract]: COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it.
In such situation, the clinicians must take important decisions, in an environment where medical ...
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 ...
Deep feature analysis in a transfer learning approach for the automatic COVID-19 screening using chest X-ray images
(Elsevier B.V., 2023)
[Abstract]: COVID-19 is a challenging disease that was declared as global pandemic in March 2020. As the main impact of this disease is located in the pulmonary regions, chest X-ray devices are very useful to understand ...
Explainable learning to analyze the outcome of COVID-19 patients using clinical data
(Elsevier B.V., 2023)
[Abstract]: Patients at high risk of contracting COVID-19 require specialized monitoring throughout their illness to ensure optimal treatment at each stage. To support this monitoring, Computer-Aided Diagnosis (CAD) methods ...
Impact of the Region of Analysis on the Performance of the Automatic Epiretinal Membrane Segmentation in OCT Images
(Springer, 2023-02-10)
[Absctract]: The Epiretinal Membrane (ERM) is an ocular pathology that can cause permanent visual loss if left untreated for long. Despite its transparency, it is possible to visualise the ERM in Optical Coherence Tomography ...