AI for healthcare

  • Attention based classification of diabetic retinopathy from Optical coherence tomography

    Diabetic Retinopathy (DR) is a common complication of diabetes that in severe cases can result in blindness. Accurate clinical treatment is imperative to prevent these cases and relies considerably on an exact diagnosis of the various symptoms of DR [3]. We aim to advance DR diagnosis by providing a practical tool to automatically classify Optical Coherence Tomography (OCT) scans for DR and to identify and localize DR-related morphological features within the scans. Our system obtains raw OCT input and only sparse clinical annotations at the volume level, which can be obtained automatically from routine electronic medical records. We designed a novel neural-network architecture, OCTTransformer, that obtains state-of-the-art classification results compared to previous models and does so with only a fraction of the training data. We base our architecture on the Attention mechanism and show this to be the driving factor for the boost in performance. For explainability reasons, we use the same model to provide pixel-wise localizations over the input scans.