A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
Autoencoders for unsupervised anomaly segmentation in brain mr images: A comparative study
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A selective inference framework is proposed to provide p-values controlling false positive rates for diffusion-based anomaly localization in images.
Unsupervised anomaly detection framework for pelvic and brain MRI reports AUC 0.97 and 0.81 on synthetic and clinical anomalies with spatial localization.
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Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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Catching MRI outliers: unsupervised detection and localization of MRI artefacts and clinical anomalies using deep learning
Unsupervised anomaly detection framework for pelvic and brain MRI reports AUC 0.97 and 0.81 on synthetic and clinical anomalies with spatial localization.