EAE-Net: Effective And Efficient x-ray joint detection
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Abstract:
The detection and localization of bone joint regions in medical X-ray images are essential for contemporary medical diagnostics. Traditional methods rely on subjective interpretation by physicians, leading to variability and potential errors. Automated bone joint detection techniques have become feasible with advancements in general-purpose object detection. However, applying these algorithms to X-ray images faces challenges due to the domain gap. To overcome these challenges, a novel framework called EAE-Net is proposed. It incorporates a Context Augment Module (CAM) to leverage global structural information and a Ghost Bottleneck Module (GBM) to reduce redundant features. The EAE-Net model achieves exceptional detection performance, striking a balance between accuracy and speed. This advancement improves efficiency, enabling clinicians to focus on critical aspects of diagnosis and treatment.
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Project Supported:
National Key R&D Program of China [2018YFB1307802]; Tianjin Science and Technology Plan Project [18PTLCSY00070]
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