Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks (2211.12717v1)

Abstract: Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep learning methods fall short of this promise; new methods continue to be evaluated on unrealistic test beds that do not reflect the complexities of downstream real-world tasks that would benefit most from reliable uncertainty quantification. We propose the RETINA Benchmark, a set of real-world tasks that accurately reflect such complexities and are designed to assess the reliability of predictive models in safety-critical scenarios. Specifically, we curate two publicly available datasets of high-resolution human retina images exhibiting varying degrees of diabetic retinopathy, a medical condition that can lead to blindness, and use them to design a suite of automated diagnosis tasks that require reliable predictive uncertainty quantification. We use these tasks to benchmark well-established and state-of-the-art Bayesian deep learning methods on task-specific evaluation metrics. We provide an easy-to-use codebase for fast and easy benchmarking following reproducibility and software design principles. We provide implementations of all methods included in the benchmark as well as results computed over 100 TPU days, 20 GPU days, 400 hyperparameter configurations, and evaluation on at least 6 random seeds each.

• The paper introduces the Retina Benchmark to evaluate Bayesian deep learning methods for diabetic retinopathy detection.
• It rigorously tests various Bayesian techniques, including MC dropout and deep ensembles, for uncertainty quantification under distribution shifts.
• Findings highlight the critical role of preprocessing and selective prediction in enhancing model reliability for medical diagnostics.

An Examination of Bayesian Deep Learning Methods for Diabetic Retinopathy Detection

The paper "Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks" presents a comprehensive examination of Bayesian approaches to deep learning, with a focus on their application in detecting diabetic retinopathy (DR). The main thrust of the research is the development and implementation of a new benchmarking suite, dubbed the Retina Benchmark, which is designed to evaluate the reliability of predictive models in safety-critical environments, particularly in medical diagnostics.

Methodology and Experimental Design

The authors craft the Retina Benchmark by leveraging two datasets: the EyePACS dataset, previously employed for the Kaggle Diabetic Retinopathy Detection Challenge, and the APTOS dataset. The former is utilized for in-domain testing, while the latter introduces a distribution shift, emulating real-world scenarios where models trained on certain population data are tested on data from different demographics and clinical conditions.

The proposed benchmark involves several tasks designed to test the robustness and reliability of Bayesian deep learning methodologies. A critical component of this work is the development of selective prediction tasks, where model predictions of uncertain cases are flagged for further human expert review. This task configuration envisions a realistic pipeline for automated DR diagnosis, where reliability and uncertainty quantification are essential.

Bayesian Deep Learning Methods Evaluated

The assessment of methods incorporates both traditional and recent Bayesian deep learning techniques. The paper discusses various approaches for Bayesian neural networks (BNNs), such as Maximum A Posteriori (MAP) estimation, variational inference including Mean-Field Variational Inference (MFVI) and its radial variant, Monte Carlo dropout, and the rank-1 parameterization. Additionally, model ensembling approaches, especially deep ensembles, are scrutinized for their potential to enhance predictive performance by capturing epistemic uncertainties more accurately.

Quantitative and Qualitative Findings

The empirical findings reveal nuanced insights into the performance landscapes of different Bayesian methods under varied evaluation conditions. Specific results show that methods like MC dropout could adequately handle uncertainty and ensure reliable performance in both in-domain and severity-shifted datasets. However, in the country shift tasks, challenges arose as models often mismanaged uncertainty estimation, demonstrated by subpar performance in selective prediction tasks for the APTOS dataset.

An intriguing aspect of the paper is its focus on preprocessing as a variable in model performance. Variation in preprocessing methods showed significant effects on downstream tasks, suggesting the need for deeper exploration into its impact on model robustness and uncertainty estimation.

Practical Implications and Future Directions

The implications of this paper are significant for the deployment of AI models in healthcare settings. Reliable uncertainty estimates are potentially life-saving in scenarios requiring high trust levels, such as medical diagnostics. The benchmark detailed in this paper provides a robust framework for evaluating Bayesian deep learning models in conditions mimicking real-world complexities and uncertainties.

The paper also opens doors for future research, particularly in enhancing Bayesian inference methods to better adapt to and generalize across distribution shifts. Moreover, the systematic benchmarking established by the authors could be extended to other domains where decision-critical predictions are necessary.

In conclusion, this research adds a meaningful chapter to the ongoing discourse on Bayesian deep learning by offering both practical tools and theoretical perspectives to improve reliable AI applications in healthcare. The rigorous methods and thoughtful benchmarking presented here will assist future researchers in deploying more robust AI systems in complex environments.