利用健康人群变异性进行脑部 FDG PET 深度学习无监督异常检测
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
AI总结：
针对脑部 FDG PET 无监督异常检测中残差图像方法的局限性，提出受 Z-scores 启发、利用健康人群变异性的新方法，在 ADNI 数据库上证明其能准确识别阿尔茨海默病相关异常。
摘要：
无监督异常检测是分析神经影像数据的一种流行方法，它能够从无标签数据中识别出多种异常。该方法依赖于构建健康外观的受试者特异性模型，将受试者的图像与之比较以检测异常。在现有文献中，异常检测通常依赖于分析受试者图像与其伪健康重建之间的残差图像。然而，这种方法存在局限性，部分原因是伪健康重建不完美且缺乏自然的阈值机制。受 Z-scores 启发，我们提出的方法利用健康人群变异性来克服这些局限性。我们在 ADNI 数据库的 FDG PET 扫描上进行的实验证明了该方法在准确识别阿尔茨海默病相关异常方面的有效性。
英文摘要：
Unsupervised anomaly detection is a popular approach for the analysis of neuroimaging data as it allows to identify a wide variety of anomalies from unlabelled data. It relies on building a subject-specific model of healthy appearance to which a subject's image can be compared to detect anomalies. In the literature, it is common for anomaly detection to rely on analysing the residual image between the subject's image and its pseudo-healthy reconstruction. This approach however has limitations partly due to the pseudo-healthy reconstructions being imperfect and to the lack of natural thresholding mechanism. Our proposed method, inspired by Z-scores, leverages the healthy population variability to overcome these limitations. Our experiments conducted on FDG PET scans from the ADNI database demonstrate the effectiveness of our approach in accurately identifying Alzheimer's disease related anomalies.
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