Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images

As a weakly supervised learning technique, multiple instance learning (MIL) has shown an advantage over supervised learning methods for automatic detection of diabetic retinopathy (DR): only the image‐level annotation is needed to achieve both detection of DR images and DR lesions, making more graded and de‐identified retinal images available for learning. However, the performance of existing studies on this technique is limited by the use of handcrafted features. The authors propose a deep MIL method for DR detection, which jointly learns features and classifiers from data and achieves a significant improvement on detecting DR images and their inside lesions. Specifically, a pre‐trained convolutional neural network is adapted to achieve the patch‐level DR estimation, and then global aggregation is used to make the classification of DR images. Further, the authors propose an end‐to‐end multi‐scale scheme to better deal with the irregular DR lesions. For detection of DR images, they achieve an area under the ROC curve of 0.925 on a subset of a Kaggle dataset, and 0.960 on Messidor. For detection of DR lesions, they achieve an F1‐score of 0.924 with sensitivity 0.995 and precision 0.863 on DIARETDB1 using the connected component‐level validation.