DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease
  Attention for Multi-Label Lesion Detection in Chest X-rays
Qing Xu Wenting Duan
Chest radiographs are the most commonly performed radiological examinations
for lesion detection. Recent advances in deep learning have led to encouraging
results in various thoracic disease detection tasks. Particularly, the
architecture with feature pyramid network performs the ability to recognise
targets with different sizes. However, such networks are difficult to focus on
lesion regions in chest X-rays due to their high resemblance in vision. In this
paper, we propose a dual attention supervised module for multi-label lesion
detection in chest radiographs, named DualAttNet. It efficiently fuses global
and local lesion classification information based on an image-level attention
block and a fine-grained disease attention algorithm. A binary cross entropy
loss function is used to calculate the difference between the attention map and
ground truth at image level. The generated gradient flow is leveraged to refine
pyramid representations and highlight lesion-related features. We evaluate the
proposed model on VinDr-CXR, ChestX-ray8 and COVID-19 datasets. The
experimental results show that DualAttNet surpasses baselines by 0.6% to 2.7%
mAP and 1.4% to 4.7% AP50 with different detection architectures. The code for
our work and more technical details can be found at
https://github.com/xq141839/DualAttNet.