A multi-branch network to detect post-operative complications following hip arthroplasty on X-ray images.

ABSTRACT: Background: Postoperative complications following total hip arthroplasty (THA) often require revision surgery. X-rays are usually used to detect such complications, but manually identifying the location of the problem and making an accurate assessment can be subjective and time-consuming. Therefore, in this study, we propose a multi-branch network to automatically detect postoperative complications on X-ray images. Methods: We developed a multi-branch network using ResNet as the backbone and two additional branches with a global feature stream and a channel feature stream for extracting features of interest. Additionally, inspired by our domain knowledge, we designed a multi-coefficient class-specific residual attention block to learn the correlations between different compli

SUBMITTER: Guo S 

PROVIDER: S-EPMC10569301 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature