Chest X-Ray (CXR) examination is a common method for assessing thoracic diseases in clinical applications. While recent advances in deep learning have enhanced the significance of visual analysis for CXR anomaly detection, current methods often miss key cues in anomaly images crucial for identifying disease regions, as they predominantly rely on unsupervised training with normal images. This letter focuses on a more practical setup in which few-shot anomaly images with only image-level labels are available during training. For this purpose, we propose WSCXR, a weakly supervised anomaly detection framework for CXR. WSCXR firstly constructs sets of normal and anomaly image features respectively. It then refines the anomaly image features by eliminating normal region features through anomaly feature mining, thus fully leveraging the scarce yet crucial features of diseased areas. Additionally, WSCXR employs a linear mixing strategy to augment the anomaly features, facilitating the training of anomaly detector with few-shot anomaly images. Experiments on two CXR datasets demonstrate the effectiveness of our approach.

翻译：胸部X光（CXR）检查是临床应用中评估胸部疾病的常用方法。尽管深度学习的最新进展提升了CXR异常视觉分析的重要性，但现有方法通常以正常图像的无监督训练为主，常忽略异常图像中识别病变区域的关键线索。本文聚焦于一种更实用的设置——在训练阶段仅有少量带图像级标签的异常图像可用。为此，我们提出WSCXR，一种针对CXR的弱监督异常检测框架。WSCXR首先分别构建正常图像特征集与异常图像特征集，随后通过异常特征挖掘消除异常图像中的正常区域特征，从而充分利用稀缺但关键的病变区域特征。此外，WSCXR采用线性混合策略增强异常特征，以促进基于少量异常图像的异常检测器训练。在两个CXR数据集上的实验验证了该方法的有效性。