Work

Advancing Lung Disease Diagnosis in 3D CT Scans

Year: 2025

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2507.00993

Authors: Li, Qingqiu, Yuan, Runtian, Hou, Junlin, Xu, Jilan, Zhang, Yuejie, Feng, Rui, Chen, Hao

Institutions: Fudan University, Hong Kong University of Science and Technology

Language: English

Topic: AI in cancer detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4416891951

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