用贝叶斯方法提升脑部MRI异常检测的可靠性与可解释性
Bayesian Autoencoder for Medical Anomaly Detection: Uncertainty-Aware Approach for Brain 2 MRI Analysis
- 基于贝叶斯变分自编码器，融合多头注意力机制
- 在BraTS2020上实现0.83的ROC与PR AUC
- 提供置信度估计，助力临床决策
在医学影像中，异常检测对医疗诊断至关重要，尤其针对可能危及生命的神经疾病。传统确定性方法难以捕捉异常检测任务中的固有不确定性。本文提出一种配备多头注意力机制的贝叶斯变分自编码器（VAE），通过贝叶斯推断同时估计认知不确定性和随机不确定性，以提升脑部磁共振成像（MRI）异常检测性能。模型在BraTS2020数据集上测试，获得0.83的ROC AUC和0.83的PR AUC。结果表明，建模不确定性是异常检测的关键，能显著提升性能与可解释性，并为临床医生提供异常预测及置信度估计，辅助医疗决策。
原文摘要 · Abstract (English)
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.
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