Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.

Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information.