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Paired and Unpaired Deep Generative Models on Multimodal Retinal Image Reconstruction
(M D P I AG, 2019-08-07)
[Abstract] This work explores the use of paired and unpaired data for training deep neural networks in the multimodal reconstruction of retinal images. Particularly, we focus on the reconstruction of fluorescein angiography ...
Automatic Identification and Characterization of the Epiretinal Membrane in OCT Images
(Optical Society of America, 2019-07-16)
[Abstract] Optical coherence tomography (OCT) is a medical image modality that is used to capture, non-invasively, high-resolution cross-sectional images of the retinal tissue. These images constitute a suitable scenario ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
Automatic Identification of Diabetic Macular Edema Using a Transfer Learning-Based Approach
(MDPI AG, 2019-07-31)
[Abstract] This paper presents a complete system for the automatic identification of pathological Diabetic Macular Edema (DME) cases using Optical Coherence Tomography (OCT) images as source of information. To do so, the ...
Automatic Wide Field Registration and Mosaicking of OCTA Images Using Vascularity Information
(Elsevier BV, 2019)
[Abstract] Optical Coherence Tomography Angiography (OCTA) constitutes a novel ophthalmological image modality that is characterized for being a non-invasive capture technique that allows a profound analysis of the vascular ...