ArXiv Preprint
Multi-label chest X-ray (CXR) recognition involves simultaneously diagnosing
and identifying multiple labels for different pathologies. Since pathological
labels have rich information about their relationship to each other, modeling
the co-occurrence dependencies between pathological labels is essential to
improve recognition performance. However, previous methods rely on state
variable coding and attention mechanisms-oriented to model local label
information, and lack learning of global co-occurrence relationships between
labels. Furthermore, these methods roughly integrate image features and label
embedding, ignoring the alignment and compactness problems in cross-modal
vector fusion.To solve these problems, a Bi-modal Bridged Graph Convolutional
Network (BB-GCN) model is proposed. This model mainly consists of a backbone
module, a pathology Label Co-occurrence relationship Embedding (LCE) module,
and a Transformer Bridge Graph (TBG) module. Specifically, the backbone module
obtains image visual feature representation. The LCE module utilizes a graph to
model the global co-occurrence relationship between multiple labels and employs
graph convolutional networks for learning inference. The TBG module bridges the
cross-modal vectors more compactly and efficiently through the GroupSum
method.We have evaluated the effectiveness of the proposed BB-GCN in two
large-scale CXR datasets (ChestX-Ray14 and CheXpert). Our model achieved
state-of-the-art performance: the mean AUC scores for the 14 pathologies were
0.835 and 0.813, respectively.The proposed LCE and TBG modules can jointly
effectively improve the recognition performance of BB-GCN. Our model also
achieves satisfactory results in multi-label chest X-ray recognition and
exhibits highly competitive generalization performance.
Guoli Wang, Pingping Wang, Jinyu Cong, Kunmeng Liu, Benzheng Wei
2023-02-22
