融合解剖结构抑制图像，提升胸片钙化结节诊断准确率
Anatomic Feature Fusion Model for Diagnosing Calcified Pulmonary Nodules on Chest X-Ray
- 结合原始图像与结构抑制图像的特征融合方法
- 诊断准确率达86.52%，AUC达0.8889
- 适合医学影像辅助诊断与放射科医生参考
胸片上肺结节的精准及时识别可区分救命的早期治疗与不必要的侵入性检查。钙化是良性结节的明确标志，是诊断的核心依据。实际中，钙化判断主要依赖医生视觉评估，存在显著解读差异。此外，肋骨、脊柱等重叠解剖结构会干扰钙化模式的精确识别。本研究提出一种钙化分类模型，通过融合原始图像及其结构抑制版本的特征，降低结构干扰，实现强诊断性能。数据来自安养大学医院，包含2,517张无病灶图像和656张结节图像（其中151个钙化，550个非钙化）。所提模型在钙化诊断中准确率达86.52%，AUC为0.8889，分别优于仅用原始图像训练的模型3.54%和0.0385。
原文摘要 · Abstract (English)
Accurate and timely identification of pulmonary nodules on chest X-rays can differentiate between life-saving early treatment and avoidable invasive procedures. Calcification is a definitive indicator of benign nodules and is the primary foundation for diagnosis. In actual practice, diagnosing pulmonary nodule calcification on chest X-rays predominantly depends on the physician's visual assessment, resulting in significant diversity in interpretation. Furthermore, overlapping anatomical elements, such as ribs and spine, complicate the precise identification of calcification patterns. This study presents a calcification classification model that attains strong diagnostic performance by utilizing fused features derived from raw images and their structure-suppressed variants to reduce structural interference. We used 2,517 lesion-free images and 656 nodule images (151 calcified nodules and 550 non-calcified nodules), all obtained from Ajou University Hospital. The suggested model attained an accuracy of 86.52% and an AUC of 0.8889 in calcification diagnosis, surpassing the model trained on raw images by 3.54% and 0.0385, respectively.
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