用于医学异常检测的贝叶斯自编码器：面向脑部2 MRI分析的不确定性感知方法
Bayesian Autoencoder for Medical Anomaly Detection: Uncertainty-Aware Approach for Brain 2 MRI Analysis
AI总结：
本文提出一种带多头注意力的贝叶斯变分自编码器，通过贝叶斯推断整合认知与偶然不确定性，在BraTS2020上实现0.83的ROC AUC和PR AUC，提升医学影像异常检测性能与可解释性。
摘要：
在医学影像中，异常检测是医疗诊断的关键组成部分，尤其是对于可能危及生命的神经系统疾病。传统的确定性方法在捕捉异常检测任务固有的不确定性方面往往力不从心。本文介绍了一种配备多头注意力机制的贝叶斯变分自编码器（VAE），用于检测脑部磁共振成像（MRI）中的异常。为了提升异常检测性能，我们通过贝叶斯推断整合了认知不确定性和偶然不确定性。该模型在BraTS2020数据集上进行了测试，结果显示ROC AUC为0.83，PR AUC为0.83。我们的论文数据表明，对不确定性进行建模是异常检测的重要组成部分，它不仅增强了性能和可解释性，还为临床医生提供了置信度估计以及异常预测，以辅助其做出医疗决策。
英文摘要：
In medical imaging, anomaly detection is a vital element of healthcare diagnostics, especially for neurological conditions which can be life-threatening. Conventional deterministic methods often fall short when it comes to capturing the inherent uncertainty of anomaly detection tasks. This paper introduces a Bayesian Variational Autoencoder (VAE) equipped with multi-head attention mechanisms for detecting anomalies in brain magnetic resonance imaging (MRI). For the purpose of improving anomaly detection performance, we incorporate both epistemic and aleatoric uncertainty estimation through Bayesian inference. The model was tested on the BraTS2020 dataset, and the findings were a 0.83 ROC AUC and a 0.83 PR AUC. The data in our paper suggests that modeling uncertainty is an essential component of anomaly detection, enhancing both performance and interpretability and providing confidence estimates, as well as anomaly predictions, for clinicians to leverage in making medical decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
