Fundus Imaging

Semi-Supervised Multi-Task Learning for Interpretable Quality Assessment of Fundus Images
We propose a semi-supervised multi-task framework for interpretable retinal image quality assessment that uses pseudo-labels for capture conditions, improving overall quality classification on EyeQ and DeepDRiD while reducing the need for detailed expert annotations.
Lucas Gabriel Telesco, Danila Nejamkin, Estefanía Mata, Francisco Filizzola, Kevin Wignall, Lucía Franco Troilo, María De Los Ángeles Cenoz, Melissa Thompson, Mercedes Leguía, Ignacio Larrabide, José Ignacio Orlando
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Semi-Supervised Multi-Task Learning for Interpretable Quality Assessment of Fundus Images