26 citations · 84 across the 12 of their papers we have counts for
12 papers
SD-RetinaNet: Topologically Constrained Semi-Supervised Retinal Lesion and Layer Segmentation in OCT
Botond Fazekas, Guilherme Aresta, Philipp Seeböck +3
Optical coherence tomography (OCT) is widely used for diagnosing and monitoring retinal diseases, such as age-related macular degeneration (AMD). The segmentation of biomarkers suc…
GARD: Gamma-based Anatomical Restoration and Denoising for Retinal OCT
Botond Fazekas, Thomas Pinetz, Guilherme Aresta +2
Optical Coherence Tomography (OCT) is a vital imaging modality for diagnosing and monitoring retinal diseases. However, OCT images are inherently degraded by speckle noise, which o…
Automatic detection and prediction of nAMD activity change in retinal OCT using Siamese networks and Wasserstein Distance for ordinality
Taha Emre, Teresa Araújo, Marzieh Oghbaie +3
Neovascular age-related macular degeneration (nAMD) is a leading cause of vision loss among older adults, where disease activity detection and progression prediction are critical f…
Comparative Analysis of Data Augmentation for Retinal OCT Biomarker Segmentation
Markus Unterdechler, Botond Fazekas, Guilherme Aresta +1
Data augmentation plays a crucial role in addressing the challenge of limited expert-annotated datasets in deep learning applications for retinal Optical Coherence Tomography (OCT)…
Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks
José Morano, Guilherme Aresta, Christoph Grechenig +2
The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the be…
RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification
José Morano, Guilherme Aresta, Hrvoje Bogunović
The caliber and configuration of retinal blood vessels serve as important biomarkers for various diseases and medical conditions. A thorough analysis of the retinal vasculature req…
SAMedOCT: Adapting Segment Anything Model (SAM) for Retinal OCT
Botond Fazekas, José Morano, Dmitrii Lachinov +2
The Segment Anything Model (SAM) has gained significant attention in the field of image segmentation due to its impressive capabilities and prompt-based interface. While SAM has al…
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
José Morano, Guilherme Aresta, Dmitrii Lachinov +3
Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these…
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Coen de Vente, Koenraad A. Vermeer, Nicolas Jaccard +33
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effectiv…
Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification
Botond Fazekas, Dmitrii Lachinov, Guilherme Aresta +3
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the lea…
SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors
Botond Fazekas, Guilherme Aresta, Dmitrii Lachinov +4
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segme…
Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening
Teresa Araújo, Guilherme Aresta, Hrvoje Bogunovic
The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly reduce the impact of this disease. However, current state-of-the-art…