利用性别信息提升肺部疾病检测，尤其改善罕见病识别
Clinical Priors Guided Lung Disease Detection in 3D CT Scans
- 分两阶段：先判性别，再按性别选分类器
- 对小样本疾病（如鳞状细胞癌）准确率显著提升
- 适合处理医疗影像中类别不平衡问题
从胸部CT扫描中准确分类肺部疾病在计算机辅助诊断系统中具有重要意义。然而，医学影像数据集常存在严重的类别不平衡问题，这会显著降低深度学习模型的性能，尤其是对少数类疾病。为此，我们提出一种性别感知的两阶段肺部疾病分类框架。该方法将性别信息显式引入疾病识别流程：第一阶段训练一个性别分类器，从CT扫描中预测患者性别；第二阶段根据预测性别，将输入图像路由至对应的性别特异性疾病分类器进行最终疾病预测。该设计使模型能更好捕捉与性别相关的影像特征，并缓解数据分布不均的影响。实验结果表明，该方法显著提升了对少数类疾病（特别是鳞状细胞癌）的识别性能，同时在其他类别上保持了有竞争力的表现。
原文摘要 · Abstract (English)
Accurate classification of lung diseases from chest CT scans plays an important role in computer-aided diagnosis systems. However, medical imaging datasets often suffer from severe class imbalance, which may significantly degrade the performance of deep learning models, especially for minority disease categories. To address this issue, we propose a gender-aware two-stage lung disease classification framework. The proposed approach explicitly incorporates gender information into the disease recognition pipeline. In the first stage, a gender classifier is trained to predict the patient's gender from CT scans. In the second stage, the input CT image is routed to a corresponding gender-specific disease classifier to perform final disease prediction. This design enables the model to better capture gender-related imaging characteristics and alleviate the influence of imbalanced data distribution. Experimental results demonstrate that the proposed method improves the recognition performance for minority disease categories, particularly squamous cell carcinoma, while maintaining competitive performance on other classes.
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