Hrvoje Bogunović

Pixel-level quality assessment can significantly enhance the reliability of fundus image evaluations, with the new EFIQA-CP method leading the way in explainability and performance.

EFIQA achieves superior image quality assessment by leveraging anatomical knowledge, offering spatial insights without the need for labeled data.

QUAM-SM reveals hidden vulnerabilities in medical image segmentation models, enhancing uncertainty quantification and clinical reliability.

Flow-matching adaptation transforms noisy OCT images into high-quality outputs, revolutionizing automated analysis in ophthalmology.