Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images
This addresses the need for interpretable AI in medical imaging to build trust among experts, though it is incremental as it applies existing weakly-supervised techniques to a specific domain.
The paper tackled the problem of localizing diabetic retinopathy lesions in retinal fundus images using weakly-supervised methods, achieving an AUC of 0.954 for classifying diseased vs. healthy images and detecting lesion areas with high accuracy comparable to supervised approaches.
Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are sceptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.