Advancing Lung Disease Diagnosis in 3D CT Scans
This work addresses lung disease diagnosis for medical imaging applications, but it is incremental as it builds on existing methods like ResNeSt50.
The paper tackled the problem of diagnosing lung disease in 3D CT scans by proposing a model that removes non-lung regions and uses ResNeSt50 with weighted cross-entropy loss, achieving a Macro F1 Score of 0.80 on a validation set.
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.