Language-Enhanced Local-Global Aggregation Network for Multi-organ Trauma Detection

Abdominal trauma is one of the leading causes of death in the elderly population and increasingly poses a global challenge. However, interpreting CT scans for abdominal trauma is considerably challenging for deep learning models. Trauma may exist in various organs presenting different shapes and morphologies. In addition, a thorough comprehension of visual cues and various types of trauma is essential, demanding a high level of domain expertise. To address these issues, this paper introduces a language-enhanced local-global aggregation network that aims to fully utilize both global contextual information and local organ-specific information inherent in images for accurate trauma detection. Furthermore, the network is enhanced by text embedding from Large Language Models (LLM). This LLM-based text embedding possesses substantial medical knowledge, enabling the model to capture anatomical relationships of intra-organ and intra-trauma connections. We have conducted experiments on one public dataset of RSNA Abdominal Trauma Detection (ATD) and one in-house dataset. Compared with existing state-of-the-art methods, the F1-score of organ-level trauma detection improves from 51.4% to 62.5% when evaluated on the public dataset and from 61.9% to 65.2% on the private cohort, demonstrating the efficacy of our proposed approach for multi-organ trauma detection. Code is available at:  https://github.com/med-air/TraumaDet