基于可解释量子自编码器的脑MRI压缩驱动异常检测
Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
AI总结：
提出一种量子自编码器（QAE），利用角度编码和变分编码器-解码器架构，通过压缩异常检测脑MRI中的肿瘤区域，在切片级和块级ROC-AUC上分别达到约0.95和0.813，优于经典自编码器和PCA基线。
摘要：
我们研究了一种量子自编码器（QAE），用于脑MRI数据中的压缩驱动异常检测。该方法利用角度编码将图像块映射到量子态，随后通过变分编码器-解码器架构训练，利用辅助垃圾量子比特丢弃信息。异常分数反映输入相对于正常数据抵抗压缩的程度，分数越高表示偏离学习的正常流形。在公开的脑MRI DICOM数据集上评估，该方法在切片级ROC-AUC达到约0.95，在块级ROC-AUC达到约0.813，优于经典自编码器和PCA基线。对学习参数的分析揭示了明显的编码器-解码器不对称性，其中有效的异常检测源于编码器内结构化的信息压缩，而非参数幅度或解码器表达能力的增加。这导致了一个可控的压缩-重建权衡，具有清晰的操作区间，支持原则性的阈值选择。定性评估进一步表明，QAE产生与肿瘤区域对齐的空间定位异常热图。结果在基线性能支持下，证明了量子自编码器提供了一种基于学习潜在表示不可压缩性的可解释且可控的异常检测机制。这项工作凸显了量子自编码器作为研究量子机器学习中压缩动力学原则性工具的潜力，对医学影像工作流程中的决策支持具有前景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
