Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
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 slic
The paper leverages recent advancements in quantum computing hardware and algorithms, specifically quantum autoencoders, to address a critical need in medical diagnostics with new computational paradigms.
This development indicates a tangible application of quantum AI in a high-stakes field like medical imaging for anomaly detection, potentially leading to more accurate and efficient diagnoses.
The diagnostic capabilities for complex medical images, especially brain MRIs, could be significantly enhanced through quantum-driven compression and anomaly detection, offering new interpretability for AI in medicine.
- · Quantum computing companies
- · Medical AI developers
- · Healthcare diagnostics
- · Neurology patients
- · Traditional image processing AI models
- · Diagnostic methodologies reliant solely on classical algorithms
Improved early detection of brain anomalies through advanced quantum AI techniques.
Accelerated development and adoption of quantum machine learning in other high-value medical applications.
Ethical and regulatory discussions emerging around quantum-enhanced AI diagnostics, especially concerning interpretability and data privacy.
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Read at arXiv cs.AI