  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.
