Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder

arXiv cs.AI Papers
quantum-autoencoder anomaly-detection brain-mri compression interpretability quantum-machine-learning

Summary

This paper presents a quantum autoencoder for compression-driven anomaly detection in brain MRI, achieving high ROC-AUC scores and outperforming classical baselines while providing interpretable anomaly heatmaps.

arXiv:2606.27411v1 Announce Type: cross Abstract: We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data. The approach leverages angle encoding to map image patches into quantum states, followed by a variational encoder-decoder architecture trained to discard information via auxiliary trash qubits. Anomaly scores reflect the degree to which inputs resist compression relative to normal data, with higher scores corresponding to deviations from the learned normal manifold. Evaluated on publicly available brain MRI DICOM datasets, the method achieves a slice-level ROC-AUC of approximately 0.95 and a patch-level ROC-AUC of approximately 0.813, outperforming classical autoencoder and PCA baselines. Analysis of the learned parameters reveals a pronounced encoder-decoder asymmetry, where effective anomaly detection arises from structured information compression within the encoder rather than increased parameter magnitude or decoder expressivity. This results in a controlled compression-reconstruction trade-off with a clear operating regime that supports principled threshold selection. Qualitative evaluation further shows that the QAE produces spatially localized anomaly heatmaps aligned with tumorous regions. The results, supported by promising baseline performances, demonstrate that quantum autoencoders provide an interpretable and controllable mechanism for anomaly detection based on incompressibility with respect to a learned latent representation. This work highlights the potential of quantum autoencoders as a principled tool for studying compression dynamics in quantum machine learning, with promising implications for decision support in medical imaging workflows.
Original Article
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# Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
Source: [https://arxiv.org/abs/2606.27411](https://arxiv.org/abs/2606.27411)
[View PDF](https://arxiv.org/pdf/2606.27411)

> Abstract:We study a quantum autoencoder \(QAE\) for compression\-driven anomaly detection in brain MRI data\. The approach leverages angle encoding to map image patches into quantum states, followed by a variational encoder\-decoder architecture trained to discard information via auxiliary trash qubits\. Anomaly scores reflect the degree to which inputs resist compression relative to normal data, with higher scores corresponding to deviations from the learned normal manifold\. Evaluated on publicly available brain MRI DICOM datasets, the method achieves a slice\-level ROC\-AUC of approximately 0\.95 and a patch\-level ROC\-AUC of approximately 0\.813, outperforming classical autoencoder and PCA baselines\. Analysis of the learned parameters reveals a pronounced encoder\-decoder asymmetry, where effective anomaly detection arises from structured information compression within the encoder rather than increased parameter magnitude or decoder expressivity\. This results in a controlled compression\-reconstruction trade\-off with a clear operating regime that supports principled threshold selection\. Qualitative evaluation further shows that the QAE produces spatially localized anomaly heatmaps aligned with tumorous regions\. The results, supported by promising baseline performances, demonstrate that quantum autoencoders provide an interpretable and controllable mechanism for anomaly detection based on incompressibility with respect to a learned latent representation\. This work highlights the potential of quantum autoencoders as a principled tool for studying compression dynamics in quantum machine learning, with promising implications for decision support in medical imaging workflows\.

## Submission history

From: Santanu Ganguly \[[view email](https://arxiv.org/show-email/4c55448e/2606.27411)\] **\[v1\]**Thu, 25 Jun 2026 12:56:20 UTC \(1,423 KB\)

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