A Scanning Laser Ophthalmoscopy Image Database and Trustworthy Retinal Disease Detection Method

Scanning laser ophthalmoscopy (SLO) images provide ophthalmologists with a non-invasive way to examine the retina for diagnostic and treatment purposes. Manual reading SLO images by ophthalmologists is a tedious task. Thus, developing trustworthy disease detection algorithms becomes urgent. However, up to now, there are no large-scale SLO image databases. In this paper, we collect and release a new SLO image dataset, named Retina-SLO, containing 7943 images of 4102 eyes from 2440 subjects with labels of three diseases, i.e., macular edema (ME), diabetic retinopathy (DR), and glaucoma. To our knowledge, Retina-SLO is the largest publicly available SLO image dataset for multiple retinal disease detection. While numerous deep learning-based methods for disease detection with medical images have been proposed, they ignore the model trust. Particularly, from a user’s perspective, the detection model is highly untrustworthy if it makes inconsistent predictions on different SLO images of the same eye captured within relatively short time intervals. To solve this issue, we propose TrustDetector, a novel disease detection method, leveraging eye-wise consistency learning and rank-based contrastive learning to ensure consistent predictions and ordered representations aligned with disease severity levels on SLO images. Experimental results show that our TrustDetector achieves better detection performances and higher consistency than the state-of-the-arts. Dataset and code are available at https://drive.google.com/drive/TrustDetector/Retina-SLO .