UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection
This addresses the risk of misdiagnosis in medical image analysis for diabetic retinopathy detection, representing an incremental improvement by focusing on uncertainty quantification.
The paper tackles the problem of predictive uncertainty in deep ensemble convolutional neural networks for diabetic retinopathy detection, proposing an uncertainty-aware test-time augmented ensemble technique to produce reliable and well-calibrated predictions, though no concrete performance numbers are provided.
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.