Automated diagnosis of pulmonary nodules in 3D PET/CT images using dual-path densely connected networks with cross-modal fusion

The proposed model outperforms state-of-the-art baselines, validating the effectiveness of its feature extraction and multi-level cross-modal interaction strategy. These findings highlight the potential of the proposed model as a robust and reliable tool in clinical settings, capable of supporting intelligent, automated diagnosis of PNs.

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Publication Details

Journal
Quantitative Imaging in Medicine and Surgery
Published
2025-12-31
DOI
https://doi.org/10.21037/qims-2025-1037
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
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article

Automated diagnosis of pulmonary nodules in 3D PET/CT images using dual-path densely connected networks with cross-modal fusion

Zhengxing Man, Qiang Lin, Junfeng Mao, Yongchun Cao et al.
Quantitative Imaging in Medicine and Surgery
Lung Cancer Diagnosis and Treatment
article

Automated diagnosis of pulmonary nodules in 3D PET/CT images using dual-path densely connected networks with cross-modal fusion

Zhengxing Man, Qiang Lin, Junfeng Mao, Yongchun Cao, Xiaodi Huang, Zhengqi Cai, Fulu Liao
article en

Abstract

The proposed model outperforms state-of-the-art baselines, validating the effectiveness of its feature extraction and multi-level cross-modal interaction strategy. These findings highlight the potential of the proposed model as a robust and reliable tool in clinical settings, capable of supporting intelligent, automated diagnosis of PNs.

Quantitative Imaging in Medicine and SurgeryVol. 16(1)
Minzu University of China (CN), Charles Sturt University (AU), Chinese People's Liberation Army (CN)
Openalex Percentile: Top 27%
Lung Cancer Diagnosis and Treatment
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