Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model uncertainty is crucial for reducing the risk of misdiagnosis. A reliable architecture should be well-calibrated to avoid over-confident predictions. To address this, we propose a UATTA-ENS: Uncertainty-Aware Test-Time Augmented Ensemble Technique for 5 Class PIRC Diabetic Retinopathy Classification to produce reliable and well-calibrated predictions.
@article{arxiv.2211.03148,
title = {UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection},
author = {Pratinav Seth and Adil Khan and Ananya Gupta and Saurabh Kumar Mishra and Akshat Bhandari},
journal= {arXiv preprint arXiv:2211.03148},
year = {2022}
}
Comments
To Appear at Medical Imaging meets NeurIPS Workshop 2022