用注意力机制提升腰椎MRI诊断准确率，助力基层医院自动识别椎间盘突出
Dual Attention Driven Lumbar Magnetic Resonance Image Feature Enhancement and Automatic Diagnosis of Herniation
- 融合通道与空间注意力机制，从双序列MRI中提取关键病变特征
- 在205例数据上实现AUC 0.969、准确率94.86%的高精度检测
- 仅需少量数据训练，适合资源有限的基层医疗机构部署
腰椎间盘突出（LDH）是常见骨骼肌肉疾病，临床管理依赖磁共振成像（MRI），但图像解读高度依赖放射科医生经验，导致诊断延迟和培训成本高。为此，本文提出一种创新的自动化LDH分类框架，利用205例患者的T1加权和T2加权MRI图像，通过数据增强及通道与空间注意力机制，提取具有临床意义的LDH特征，并生成标准化诊断输出，辅助医生高效决策。该框架在LDH检测中达到0.969的AUC-ROC值和0.9486的准确率，实验结果验证了其有效性。本框架仅需少量训练数据即实现高诊断精度，有望提升基层医院的LDH识别能力。
原文摘要 · Abstract (English)
Lumbar disc herniation (LDH) is a common musculoskeletal disease that requires magnetic resonance imaging (MRI) for effective clinical management. However, the interpretation of MRI images heavily relies on the expertise of radiologists, leading to delayed diagnosis and high costs for training physicians. Therefore, this paper proposes an innovative automated LDH classification framework. To address these key issues, the framework utilizes T1-weighted and T2-weighted MRI images from 205 people. The framework extracts clinically actionable LDH features and generates standardized diagnostic outputs by leveraging data augmentation and channel and spatial attention mechanisms. These outputs can help physicians make confident and time-effective care decisions when needed. The proposed framework achieves an area under the receiver operating characteristic curve (AUC-ROC) of 0.969 and an accuracy of 0.9486 for LDH detection. The experimental results demonstrate the performance of the proposed framework. Our framework only requires a small number of datasets for training to demonstrate high diagnostic accuracy. This is expected to be a solution to enhance the LDH detection capabilities of primary hospitals.
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