@inproceedings{418f61f8968f417dba7b1f5540afdaa4,
title = "LungAide: An Effective 3D Transformer V-Net for Lung Nodule Detection",
abstract = "Existing lung nodule detection methods on computed tomography (CT) have the problems of missing small nodules and losing spatial information. This paper proposes LungAide, a novel 3D Transformer V-Net for accurate lung nodule detection in chest CT scans, with a particular focus on small nodules. The proposed architecture integrates the encoder-decoder network and concatenation operation of V-Net with the Transformer blocks featuring a lightweight self-attention mechanism with a Bi-Path downsampling, enabling to preserve spatial information and capture long-range dependencies between lung nodules. To validate the effectiveness of LungAide, experiments are conducted on the LUNA16 dataset, which achieving a Competition Performance Metric of 0.915. Comparative and ablation studies confirm that LungAide effectively reduces false positives while enhancing sensitivity.",
keywords = "Deep Learning, Lung Nodule Detection, Transformer, V-Net",
author = "Xinyuan Gao and Lei Dong and Xingwang Liu and Sijie Yin and Hao Chen",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 9th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2025 ; Conference date: 31-10-2025 Through 04-11-2025",
year = "2026",
doi = "10.1007/978-981-95-6730-0\_13",
language = "English",
isbn = "9789819567294",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "177--189",
editor = "Hongbin Ma and Bin Xin and Jinhua She and Yaping Dai",
booktitle = "Advanced Computational Intelligence and Intelligent Informatics - 9th International Workshop, IWACIII 2025, Proceedings",
address = "Germany",
}