Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resources

arXiv:2402.09747v1 Announce Type: cross
Abstract: Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses. However, the acquisition of Retinal OCT images often presents challenges stemming from …

abstract advancement analysis application arxiv automated clinicians cs.cv cs.lg data decision disease eess.iv ensemble health image images insights making optical posterior quantitative recognition researchers resources segment type