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.

针对少样本异常图像的弱监督胸部 X 光检测方法（WSCXR）的研究。WSCXR 通过利用异常特征挖掘消除正常区域特征并采用线性混合策略增强异常特征，从而充分利用疾病区域的关键特征，实现了对异常图像的有效检测。