Abstract

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Title:: Context-aware lightweight FAST for joint recognition of ascites and anatomical structures

Author(s):: JING Juan1; TANG Jiajie2; XIAO Jingjing2; WANG Wei1; JIANG Xiaoming1; 1. School of Life Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China 2. Bio-Med Informatics Research Centre & Clinical Research Centre, Xinqiao Hospital, Army Medical University, Chongqing 40037, China

Keywords:: Keywords: focused assessment with sonography for trauma abdominal ultrasonography context-aware multi-task learning lightweight

Abstract:: Abstract: Focused assessment with sonography for trauma (FAST) technology highly depends on operator experience, and existing deep learning-based models encounter key technical challenges including inadequate multi-task collaboration, low computational efficiency, and insufficient ability to identify and locate lesions. To address the above issues, a context-aware lightweight multi-task model (CALM-FAST) is proposed, which combines feature sharing mechanism with task-specific representation to achieve joint optimization of peritoneal effusion segmentation and anatomical structure detection. The proposed model introduces a context-aware lightweight module into the backbone network, and integrates fast convolution and feature fusion strategies, which reduces the computational complexity to 6.90 GFLOPs while preserving global feature representation capability. Furthermore, a multi-task joint loss function is used to mitigate the data imbalance issue in FAST. In addition, a real-world dataset containing 693 ultrasound images is constructed, and a hierarchical transfer learning strategy is adopted to enhance the generalization performance. Results show that the proposed method achieves a mAP@0.5 of 93.0% and a mAP@0.5:0.95 of 64.5% in the anatomical structure detection task. For the peritoneal effusion segmentation, the IoU is increased to 25.9%, which is 9.0% higher than the baseline model.