activity
20182026
most citedSegmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification

26 citations · 63 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2026

A Visual Grounding Benchmark for Retinal OCT with Multimodal Large Language Models

Ronald Fecso, José Morano, Botond Fazekas +6

Multimodal large language models (MLLMs) can answer clinical questions about medical images, but trustworthy use requires visual grounding: models should localize the pathology the…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025★ 22 cited

MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis

José Morano, Botond Fazekas, Emese Sükei +7

Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing…

eess.IV2024

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)…

eess.IV2023★ 2 cited

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…

eess.IV2022★ 26 cited

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…

eess.IV2022

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…

cs.SD2018★ 13 cited

A Multi-modal Deep Neural Network approach to Bird-song identification

Botond Fazeka, Alexander Schindler, Thomas Lidy +1

We present a multi-modal Deep Neural Network (DNN) approach for bird song identification. The presented approach takes both audio samples and metadata as input. The audio is fed in…