Collaborative Deep Learning for Privacy Preserving Diabetic Retinopathy Detection

Convolutional Neural Networks (CNNs) are an emerging research area for detection of Diabetic Retinopathy (DR) development in fundus images with highly reliable results. However, its accuracy depends on the availability of big datasets to train such a deep network. Due to the privacy concerns, the strict rules on medical data limit accessibility of images in publicly available datasets. In this paper, we propose a collaborative learning approach to train CNN models with multiple datasets while preserving the privacy of datasets in a distributed learning environment without sharing them. First, CNN networks are trained with private datasets, and tested with the same publicly available images. Based on their initial accuracies, the CNN model with the lowest performance among datasets is forwarded to second lowest performed dataset to retrain it using the transfer learning approach. Then, the retrained network is forwarded to next dataset. This procedure is repeated for each dataset from the lowest performed dataset to the highest. With this ascending chain order fashion, the network is retrained again and again using different datasets and its performance is improved over the time. Based on our experimental results on five different retina image datasets, DR detection accuracy is increased to 93.5% compared with the accuracies of merged datasets (84%) and individual datasets (73%, 78%, 83%, 85%).