Jing Yang , Lianxin Wang , Chen Lin , Jiacheng Wang , Liansheng Wang
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SAM employs limited positional supervision to guide the model in focusing on the hip joint region and reducing background interference. SCM integrates information from radiological reports, utilizes prior knowledge from large language models to extract critical information related to ND-FNF, and guides the model to learn relevant visual representations. During inference, the model only requires the whole X-ray image for accurate diagnosis without additional information. The model was validated on datasets from four different centers, showing consistent accuracy and robustness. 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Most deep learning-based methods for diagnosing ND-FNF rely on cropped images, necessitating manual annotation of the hip location, which increases annotation costs. To address this challenge, we propose Dual Domain Knowledge Guidance (DDKG), which harnesses spatial and semantic domain knowledge to guide the model in acquiring robust representations of ND-FNF across the whole X-ray image. Specifically, DDKG comprises two key modules: the Spatial Aware Module (SAM) and the Semantic Coordination Module (SCM). SAM employs limited positional supervision to guide the model in focusing on the hip joint region and reducing background interference. SCM integrates information from radiological reports, utilizes prior knowledge from large language models to extract critical information related to ND-FNF, and guides the model to learn relevant visual representations. During inference, the model only requires the whole X-ray image for accurate diagnosis without additional information. The model was validated on datasets from four different centers, showing consistent accuracy and robustness. 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引用次数: 0
摘要
X 射线是诊断骨折的主要工具,对于确定骨折的类型、位置和严重程度至关重要。然而,由于存在细微的裂缝和复杂的解剖结构,非移位股骨颈骨折(ND-FNF)的识别面临挑战。大多数基于深度学习的 ND-FNF 诊断方法都依赖于裁剪图像,需要人工标注髋关节位置,这增加了标注成本。为了应对这一挑战,我们提出了双领域知识指导(DDKG),利用空间和语义领域知识指导模型在整个 X 光图像中获取 ND-FNF 的稳健表示。具体来说,DDKG 包括两个关键模块:空间感知模块(SAM)和语义协调模块(SCM)。空间感知模块采用有限的位置监督来引导模型聚焦于髋关节区域并减少背景干扰。SCM 整合了放射报告中的信息,利用大型语言模型中的先验知识提取与 ND-FNF 相关的关键信息,并引导模型学习相关的视觉表征。在推理过程中,该模型只需要整个 X 光图像就能准确诊断,无需额外信息。该模型在四个不同中心的数据集上进行了验证,显示出一致的准确性和鲁棒性。代码和模型可在 https://github.com/Yjing07/DDKG 上获取。
DDKG: A Dual Domain Knowledge Guidance strategy for localization and diagnosis of non-displaced femoral neck fractures
X-ray is the primary tool for diagnosing fractures, crucial for determining their type, location, and severity. However, non-displaced femoral neck fractures (ND-FNF) can pose challenges in identification due to subtle cracks and complex anatomical structures. Most deep learning-based methods for diagnosing ND-FNF rely on cropped images, necessitating manual annotation of the hip location, which increases annotation costs. To address this challenge, we propose Dual Domain Knowledge Guidance (DDKG), which harnesses spatial and semantic domain knowledge to guide the model in acquiring robust representations of ND-FNF across the whole X-ray image. Specifically, DDKG comprises two key modules: the Spatial Aware Module (SAM) and the Semantic Coordination Module (SCM). SAM employs limited positional supervision to guide the model in focusing on the hip joint region and reducing background interference. SCM integrates information from radiological reports, utilizes prior knowledge from large language models to extract critical information related to ND-FNF, and guides the model to learn relevant visual representations. During inference, the model only requires the whole X-ray image for accurate diagnosis without additional information. The model was validated on datasets from four different centers, showing consistent accuracy and robustness. Codes and models are available at https://github.com/Yjing07/DDKG.
期刊介绍:
Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.