5 papers
FunPiQ: A New Benchmark for Pixel-Level Quality Assessment in Fundus Images
Pengwei Wang, José Morano, Virginia Mares +1
Color fundus photography (CFP) is the most common ophthalmic imaging modality for large-scale screening. However, it is highly susceptible to degradations, making robust fundus ima…
EFIQA: Explainable Fundus Image Quality Assessment via Anatomical Priors
Pengwei Wang, José Morano, Qian Wan +1
Image quality control is vital for a wide range of downstream applications. Deep learning-based image quality assessment methods typically train classifiers on dataset-specific qua…
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…
RetFiner: A Vision-Language Refinement Scheme for Retinal Foundation Models
Ronald Fecso, José Morano, Ursula Schmidt-Erfurth +1
The rise of imaging techniques such as optical coherence tomography (OCT) and advances in deep learning (DL) have enabled clinicians and researchers to streamline retinal disease s…
RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification
José Morano, Guilherme Aresta, Hrvoje BogunoviÄ
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…