[2603.25607] DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25607 (cs) [Submitted on 26 Mar 2026] Title:DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial Authors:Zhenchen Zhu, Ge Hu, Weixiong Tan, Kai Gao, Chao Sun, Zhen Zhou, Kepei Xu, Wei Han, Meixia Shang, Xiaoming Qiu, Yiqing Tan, Jinhua Wang, Zhoumeng Ying, Li Peng, Wei Song, Lan Song, Zhengyu Jin, Nan Hong, Yizhou Yu View a PDF of the paper titled DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial, by Zhenchen Zhu and 18 other authors View PDF Abstract:The widespread adoption of CT has notably increased the number of detected lung nodules. However, current deep learning methods for classifying benign and malignant nodules often fail to comprehensively integrate global and local features, and most of them have not been validated through clinical trials. To address this, we developed DeepFAN, a transformer-based model trained on over 10K pathology-confirmed nodules and further conducted a multi-reader, multi-case clinical trial to evaluate its efficacy in assisting junior radiologists. DeepFAN achieved diagnostic area under the curve (AUC) of 0.939 (95% CI 0.930-0.948) on an internal test set and 0.954 (95% CI 0.934-0.973) on the clinical tria...