Advancing Lung Disease Diagnosis in 3D CT Scans
Journal:
arXiv
Published Date:
Jul 1, 2025
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.
