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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 ...
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 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, ...
Fully Automatic Method for the Visual Acuity Estimation Using OCT Angiographies
(MDPI AG, 2020-09-04)
[Abstract]
In this work we propose the automatic estimation of the visual acuity of patients with retinal vein occlusion using Optical Coherence Tomography by Angiography (OCTA) images. To do this, we first extract the ...
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 ...
Fully Automatic Retinal Vascular Tortuosity Assessment Integrating Domain-Related Information
(MDPI AG, 2020-08-21)
[Abstract]
The fundus of the eye is the only part of the human body that allows a direct non-invasive observation of the circulatory system. Retinal vascular tortuosity presents a valuable potential for diagnostic and ...
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 ...
Joint Optic Disc and Cup Segmentation Using Self-Supervised Multimodal Reconstruction Pre-Training
(MDPI AG, 2020-08-20)
[Abstract]
The analysis of the optic disc and cup in retinal images is important for the early diagnosis of glaucoma. In order to improve the joint segmentation of these relevant retinal structures, we propose a novel ...
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 ...