Deep neural network (DNN) models for retinopathy have estimated predictive accuracies in the mid-to-high 90%. However, the following aspects remain unaddressed: State-of-the-art models are complex and require substantial computational infrastructure to train and deploy; The reliability of predictions can vary widely. In this paper, we focus on these aspects and propose a form of iterative knowledge distillation(IKD), called IKD+ that incorporates a tradeoff between size, accuracy and reliability. We investigate the functioning of IKD+ using two widely used techniques for estimating model calibration (Platt-scaling and temperature-scaling), using the best-performing model available, which is an ensemble of EfficientNets with approximately 100M parameters. We demonstrate that IKD+ equipped with temperature-scaling results in models that show up to approximately 500-fold decreases in the number of parameters than the original ensemble without a significant loss in accuracy. In addition, calibration scores (reliability) for the IKD+ models are as good as or better than the base mode
@article{arxiv.2303.02310,
title = {IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification},
author = {Shreyas Bhat Brahmavar and Rohit Rajesh and Tirtharaj Dash and Lovekesh Vig and Tanmay Tulsidas Verlekar and Md Mahmudul Hasan and Tariq Khan and Erik Meijering and Ashwin Srinivasan},
journal= {arXiv preprint arXiv:2303.02310},
year = {2024}
}
Comments
Submitted to IEEE International Conference on Image Processing (ICIP 2023)