44 citations · 129 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…