Advancing Lung Disease Diagnosis in 3D CT Scans

• URL: http://arxiv.org/abs/2507.00993v1
• Date: Tue, 01 Jul 2025 17:44:53 GMT
• Title: Advancing Lung Disease Diagnosis in 3D CT Scans
• Authors: Qingqiu Li, Runtian Yuan, Junlin Hou, Jilan Xu, Yuejie Zhang, Rui Feng, Hao Chen,
• Abstract summary: We analyze the characteristics of 3D CT scans and remove non-lung regions, which helps the model focus on lesion-related areas.<n>Our model achieves a Macro F1 Score of 0.80 on the validation set of the Fair Disease Diagnosis Challenge.
• Score: 19.844531606142496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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