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: \url{https://github.com/ci-ber/RA}.

翻译：医学影像数据日益增长的复杂性凸显了对先进异常检测方法的需求，以自动识别多种病理特征。当前的方法在捕捉广泛异常方面面临挑战，通常仅限于脑部扫描中特定病变类型的检测。为解决这一问题，我们提出了一种新颖的无监督方法——名为“反向自编码器（Reversed Auto-Encoders, RA）”，旨在生成逼真的伪健康重建图像，从而实现对更广泛病理特征的检测。我们在多种成像模态上评估了所提方法，包括脑部磁共振成像（MRI）、儿童腕部X光片和胸部X光片，并展示了与现有最先进方法相比在异常检测中的优越性能。我们的无监督异常检测方法通过识别更广泛的未知病理特征，有望提升医疗影像的诊断准确性。我们的代码开源提供于：\url{https://github.com/ci-ber/RA}。