面向医学领域的持续视觉异常检测
Towards Continual Visual Anomaly Detection in the Medical Domain
AI总结：
本研究首次探索医学领域持续学习场景下的视觉异常检测应用，提出PatchCore模型的持续学习版本PatchCoreCL，基于BMAD数据集评估显示其性能与特定任务模型相当且遗忘值低于1%，验证了持续学习的可行性与潜力。
摘要：
视觉异常检测（VAD）旨在识别异常图像并精确定位相应的异常区域，训练过程仅依赖正常数据。这种方法已被证明在制造等领域至关重要，最近在医学领域也很关键，因为该领域需要准确且可解释的检测。尽管其重要性显著，但随时间演变的输入数据分布带来的影响却很少受到关注，而这种变化会大幅降低模型性能。特别是考虑到医学影像数据的动态和演变特性，持续学习（CL）提供了一个自然且有效的框架，可在保留已有知识的同时增量式调整模型。本研究首次探索了VAD模型在医学领域持续学习场景中的应用。在这项工作中，我们使用了成熟的PatchCore模型的持续学习版本，称为PatchCoreCL，并通过BMAD（一个包含图像级和像素级标注的真实世界医学影像数据集）评估其性能。我们的结果表明，PatchCoreCL是一种有效的解决方案，其性能与特定任务模型相当，遗忘值低于1%，突显了持续学习在医学影像自适应视觉异常检测中的可行性和潜力。
英文摘要：
Visual Anomaly Detection (VAD) seeks to identify abnormal images and precisely localize the corresponding anomalous regions, relying solely on normal data during training. This approach has proven essential in domains such as manufacturing and, more recently, in the medical field, where accurate and explainable detection is critical. Despite its importance, the impact of evolving input data distributions over time has received limited attention, even though such changes can significantly degrade model performance. In particular, given the dynamic and evolving nature of medical imaging data, Continual Learning (CL) provides a natural and effective framework to incrementally adapt models while preserving previously acquired knowledge. This study explores for the first time the application of VAD models in a CL scenario for the medical field. In this work, we utilize a CL version of the well-established PatchCore model, called PatchCoreCL, and evaluate its performance using BMAD, a real-world medical imaging dataset with both image-level and pixel-level annotations. Our results demonstrate that PatchCoreCL is an effective solution, achieving performance comparable to the task-specific models, with a forgetting value less than a 1%, highlighting the feasibility and potential of CL for adaptive VAD in medical imaging.
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