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Deep neural network (DNN) models for retinopathy have estimated predictiveaccuracies in the mid-to-high 90%. However, the following aspects remainunaddressed: State-of-the-art models are complex and require substantialcomputational infrastructure to train and deploy; The reliability ofpredictions can vary widely. In this paper, we focus on these aspects andpropose a form of iterative knowledge distillation(IKD), called IKD+ thatincorporates a tradeoff between size, accuracy and reliability. We investigatethe functioning of IKD+ using two widely used techniques for estimating modelcalibration (Platt-scaling and temperature-scaling), using the best-performingmodel available, which is an ensemble of EfficientNets with approximately 100Mparameters. We demonstrate that IKD+ equipped with temperature-scaling resultsin models that show up to approximately 500-fold decreases in the number ofparameters than the original ensemble without a significant loss in accuracy.In addition, calibration scores (reliability) for the IKD+ models are as goodas or better than the base mode

IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification