提升3D肺部CT诊断准确率，聚焦病灶区域并优化不平衡分类。
Advancing Lung Disease Diagnosis in 3D CT Scans
- 剔除非肺部区域，专注病灶，降低计算开销。
- 在公平诊断挑战验证集上达0.80宏F1分数。
- 适合关注肺部疾病精准诊断的研究者与临床医生。
为提升胸部CT扫描中肺部疾病的诊断准确性，我们提出一种简单而高效的方法。首先分析3D CT扫描特性，移除非肺区域，使模型更关注病变区域，同时降低计算成本。采用ResNeSt50作为强特征提取器，并使用加权交叉熵损失缓解类别不平衡问题，尤其针对代表性不足的鳞状细胞癌类别。模型在公平疾病诊断挑战验证集上取得0.80的宏F1分数，展现出对多种肺部疾病的有效区分能力。
原文摘要 · Abstract (English)
To enable more accurate diagnosis of lung disease in chest CT scans, we propose a straightforward yet effective model. Firstly, we analyze the characteristics of 3D CT scans and remove non-lung regions, which helps the model focus on lesion-related areas and reduces computational cost. We adopt ResNeSt50 as a strong feature extractor, and use a weighted cross-entropy loss to mitigate class imbalance, especially for the underrepresented squamous cell carcinoma category. Our model achieves a Macro F1 Score of 0.80 on the validation set of the Fair Disease Diagnosis Challenge, demonstrating its strong performance in distinguishing between different lung conditions.
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