Retinopathy detection using optical coherence tomography (OCT) images has greatly advanced with computer vision but traditionally requires extensive annotated data, which is time-consuming and expensive. To address this issue, we propose a novel contrastive graph regularization method for detecting retinopathies with less labeled OCT images. This method combines class prediction probabilities and embedded image representations for training, where the two representations interact and co-evolve within the same training framework. Specifically, we leverage memory smoothing constraints to improve pseudo-labels, which are aggregated by nearby samples in the embedding space, effectively reducing overfitting to incorrect pseudo-labels. Our method, using only 80 labeled OCT images, outperforms existing methods on two widely used OCT datasets, with classification accuracy exceeding 0.96 and an Area Under the Curve (AUC) value of 0.998. Additionally, compared to human experts, our method achieves expert-level performance with only 80 labeled images and surpasses most experts with just 160 labeled images.