A multimodal method based on cross-attention and convolution for
  postoperative infection diagnosis
Xianjie Liu Hongwei Shi
Postoperative infection diagnosis is a common and serious complication that
generally poses a high diagnostic challenge. This study focuses on PJI, a type
of postoperative infection. X-ray examination is an imaging examination for
suspected PJI patients that can evaluate joint prostheses and adjacent tissues,
and detect the cause of pain. Laboratory examination data has high sensitivity
and specificity and has significant potential in PJI diagnosis. In this study,
we proposed a self-supervised masked autoencoder pre-training strategy and a
multimodal fusion diagnostic network MED-NVC, which effectively implements the
interaction between two modal features through the feature fusion network of
CrossAttention. We tested our proposed method on our collected PJI dataset and
evaluated its performance and feasibility through comparison and ablation
experiments. The results showed that our method achieved an ACC of 94.71% and
an AUC of 98.22%, which is better than the latest method and also reduces the
number of parameters. Our proposed method has the potential to provide
clinicians with a powerful tool for enhancing accuracy and efficiency.