MFA-DSLNet: Multi-scale Fusion Attention Mechanism with Dual-threshold Self-paced Learning for Pediatric Pneumonia Diagnosis

Chest X-ray (CXR) is crucial to the early diagnosis and treatment of pneumonia in children while still facing the viral and bacterial pneumonia identification challenge. Although convolutional neural networks (CNNs) can identify the concealed features between viral and bacterial pneumonia, the influence on the network accuracy led by morphological lesion size variation and confounded visual imaging findings is not explicitly addressed. To address these issues, we propose a novel framework MFA-DSLNet to classify viral and bacterial CXR in childhood pneumonia. It comprises two functional blocks. First, a Multiscale Fusion Attention (MFA) mechanism fuses low-level and high-level features to enrich the information in feature maps. Then, two attention modules, namely Context Attention (CA) and Spatial Position Attention (SPA), attempt to extract effective latent information from the channel and spatial dimension respectively to tackle the redundancy problem in feature maps. To facilitate the identification of hard cases, Dual-threshold Self-paced Learning (DSL) is proposed to dynamically adapt the training sequence of samples. Empirically, MFA-DSLNet achieves a state-of-the-art 93.33% accuracy, with a 2.33% improvement to the previous works on the ChestXray2017 dataset.