Abstract
Chest X-ray is widely used to diagnose lung diseases. Due to the demand for accelerating analysis and interpretation to reduce the workload of radiologists, there has been a growing interest in building automated systems of chest X-ray abnormality localization. However, fully supervised methods usually require well-trained radiologists to annotate bounding boxes manually, which is labor-intensive and time-consuming. As a result, weakly supervised chest X-ray abnormality localization is gaining increasing attention because it only requires image-level annotations. Existing weakly supervised object localization (WSOL) techniques, which typically utilize class activation maps, often result in incomplete coverage and fragmentation of the objects and rely on class-specific classification accuracy. In this study, we propose a novel WSOL framework for chest X-ray abnormality localization that uses VMamba as the backbone and integrates three practical components to improve localization accuracy. First, we propose a non-linear modulation module to refine Foreground Prediction Maps (FPM) by expanding the foreground activation region and enhancing its continuity. Second, we design an FPM fusion module to strengthen the foreground and suppress the background, thereby improving their separability in chest X-ray images. Third, we craft a novel foreground control loss that regulates the feature maps to refine the background and foreground activation for better foreground identification. The proposed method is evaluated on two commonly used chest X-ray datasets, the NIH chest X-ray dataset and the RSNA dataset, and demonstrates superior performance over six state-of-the-art WSOL methods. In addition, the robustness and applicability of the proposed method are evaluated using three additional datasets with varying modalities and image quality.