The global ramifications of the COVID-19 pandemic remain significant,
exerting persistent pressure on nations even three years after its initial
outbreak. Deep learning models have shown promise in improving COVID-19
diagnostics but require diverse and larger-scale datasets to improve
performance. In this paper, we introduce COVIDx CXR-4, an expanded
multi-institutional open-source benchmark dataset for chest X-ray image-based
computer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on the
previous COVIDx CXR-3 dataset by increasing the total patient cohort size by
greater than 2.66 times, resulting in 84,818 images from 45,342 patients across
multiple institutions. We provide extensive analysis on the diversity of the
patient demographic, imaging metadata, and disease distributions to highlight
potential dataset biases. To the best of the authors' knowledge, COVIDx CXR-4
is the largest and most diverse open-source COVID-19 CXR dataset and is made
publicly available as part of an open initiative to advance research to aid
clinicians against the COVID-19 disease.

COVIDx CXR-4 是一种新的开源数据集，通过增加总患者群体大小，从多个机构收集来自 45,342 位患者的 84,818 张胸部 X 射线图像，以改善 COVID-19 的计算机辅助诊断，从而提高 COVID-19 诊断的性能。

COVIDx CXR-4：用于基于胸部 X 射线图片的辅助 COVID-19 诊断的开源多机构扩展基准数据集

COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark  Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics

Estimated uncertainty by approximate posteriors in Bayesian neural networks
are prone to miscalibration, which leads to overconfident predictions in
critical tasks that have a clear asymmetric cost or significant losses. Here,
we extend the approximate inference for the loss-calibrated Bayesian framework
to dropweights based Bayesian neural networks by maximising expected utility
over a model posterior to calibrate uncertainty in deep learning. Furthermore,
we show that decisions informed by loss-calibrated uncertainty can improve
diagnostic performance to a greater extent than straightforward alternatives.
We propose Maximum Uncertainty Calibration Error (MUCE) as a metric to measure
calibrated confidence, in addition to its prediction especially for high-risk
applications, where the goal is to minimise the worst-case deviation between
error and estimated uncertainty. In experiments, we show the correlation
between error in prediction and estimated uncertainty by interpreting
Wasserstein distance as the accuracy of prediction. We evaluated the
effectiveness of our approach to detecting Covid-19 from X-Ray images.
Experimental results show that our method reduces miscalibration considerably,
without impacting the models accuracy and improves reliability of
computer-based diagnostics.

本文提出了一种基于概率贝叶斯方法的神经网络预测置信度校准方案，最大化置信度期望效用，用最大置信度校准误差度量预测的置信度和错误之间的关系，并在 COVID-19 诊断中测试取得了显著的效果。