Advancing Lung Disease Diagnosis in 3D CT Scans
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908429538295808 |
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| author | Li, Qingqiu Yuan, Runtian Hou, Junlin Xu, Jilan Zhang, Yuejie Feng, Rui Chen, Hao |
| author_facet | Li, Qingqiu Yuan, Runtian Hou, Junlin Xu, Jilan Zhang, Yuejie Feng, Rui Chen, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_00993 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Lung Disease Diagnosis in 3D CT Scans Li, Qingqiu Yuan, Runtian Hou, Junlin Xu, Jilan Zhang, Yuejie Feng, Rui Chen, Hao Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Advancing Lung Disease Diagnosis in 3D CT Scans |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.00993 |