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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Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
A selective inference framework is proposed to provide p-values controlling false positive rates for diffusion-based anomaly localization in images.