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UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection

arXiv 2211.03148 ↗First posted 6 Nov 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 18

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  4. 2017Deep Neural Networks as Gaussian Processescited by 617
  5. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  6. 2019Measuring Calibration in Deep Learningcited by 443
  7. 2015Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networkscited by 295
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  9. 2020Uncertainty Quantification and Deep Ensemblescited by 138
  10. 2018Uncertainty in Neural Networks: Approximately Bayesian Ensemblingcited by 94
  11. 2019Calibration tests in multi-class classification: A unifying frameworkcited by 79
  12. 2019Analyzing the Role of Model Uncertainty for Electronic Health Recordscited by 41
6 more
  1. 2017Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus imagescited by 18
  2. 2021Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigationcited by 10
  3. 2022Uncertainty-aware deep learning methods for robust diabetic retinopathy classificationcited by 8
  4. 2020Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensemblescited by 5
  5. 2021Uncertainty-Aware Boosted Ensembling in Multi-Modal Settingscited by 4
  6. 2021Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Datacited by 2

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