Bayesian Autoencoder for Medical Anomaly Detection: Uncertainty-Aware Approach for Brain 2 MRI Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917994524835840 |
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| author | Roy, Dip |
| author_facet | Roy, Dip |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_15562 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Autoencoder for Medical Anomaly Detection: Uncertainty-Aware Approach for Brain 2 MRI Analysis Roy, Dip Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Bayesian Autoencoder for Medical Anomaly Detection: Uncertainty-Aware Approach for Brain 2 MRI Analysis |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.15562 |