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arXiv · 2507.00993

Advancing Lung Disease Diagnosis in 3D CT Scans

Qingqiu Li·Runtian Yuan·Junlin Hou·Jilan Xu·Yuejie Zhang·Rui Feng·Hao Chen
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Abstract

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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Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen. 2025-07-01. Advancing Lung Disease Diagnosis in 3D CT Scans. https://arxiv.org/abs/2507.00993

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