The increasing complexity of medical imaging data underscores the need for
advanced anomaly detection methods to automatically identify diverse
pathologies. Current methods face challenges in capturing the broad spectrum of
anomalies, often limiting their use to specific lesion types in brain scans. To
address this challenge, we introduce a novel unsupervised approach, termed
\textit{Reversed Auto-Encoders (RA)}, designed to create realistic
pseudo-healthy reconstructions that enable the detection of a wider range of
pathologies. We evaluate the proposed method across various imaging modalities,
including magnetic resonance imaging (MRI) of the brain, pediatric wrist X-ray,
and chest X-ray, and demonstrate superior performance in detecting anomalies
compared to existing state-of-the-art methods. Our unsupervised anomaly
detection approach may enhance diagnostic accuracy in medical imaging by
identifying a broader range of unknown pathologies. Our code is publicly
available at: https://github.com/ci-ber/RA.

通过引入一种新颖的无监督方法 RA，可自动生成逼真的健康影像重构，从而识别更广泛的病理性异常，超过现有先进方法在多种影像模态中的表现，提高医学影像的诊断准确性。

面向医学影像的通用无监督异常检测

Towards Universal Unsupervised Anomaly Detection in Medical Imaging

Anomaly detection methods have demonstrated remarkable success across various
applications. However, assessing their performance, particularly at the
pixel-level, presents a complex challenge due to the severe imbalance that is
most commonly present between normal and abnormal samples. Commonly adopted
evaluation metrics designed for pixel-level detection may not effectively
capture the nuanced performance variations arising from this class imbalance.
In this paper, we dissect the intricacies of this challenge, underscored by
visual evidence and statistical analysis, leading to delve into the need for
evaluation metrics that account for the imbalance. We offer insights into more
accurate metrics, using eleven leading contemporary anomaly detection methods
on twenty-one anomaly detection problems. Overall, from this extensive
experimental evaluation, we can conclude that Precision-Recall-based metrics
can better capture relative method performance, making them more suitable for
the task.

在本研究中，我们通过可视证据和统计分析剖析了像素级异常检测中存在的正常样本和异常样本严重不平衡的复杂挑战，并深入探讨了需要考虑不平衡的评估指标的需求。通过对 21 个异常检测问题上使用 11 种领先的现代异常检测方法进行广泛实验评估，我们可以得出结论，基于 Precision-Recall 的指标可以更好地捕捉方法性能的相对关系，使其更适用于这一任务。