Low-Shot Deep Learning of Diabetic Retinopathy

This study evaluates the effectiveness of low-shot deep learning methods in automated retinal diagnostics when training data is limited, compared to traditional deep learning approaches. Using the EyePACS dataset, it was found that low-shot methods performed better with fewer training samples, achieving higher accuracy in diabetic retinopathy classification when only a small number of images were available. These results suggest that low-shot learning could be particularly beneficial for diagnosing rare retinal diseases and addressing biases in AI training datasets.