SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection
<|reference_start|>SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection: Laryngo-pharyngeal cancer (LPC) is a highly fatal malignant disease affecting the head and neck region. Previous studies on endoscopic tumor detection, particularly those leveraging dual-branch network architectures, have shown significant advancements in tumor detection. These studies highlight the potential of dual-branch networks in improving diagnostic accuracy by effectively integrating global and local (lesion) feature extraction. However, they are still limited in their capabilities to accurately locate the lesion region and capture the discriminative feature information between the global and local branches. To address these issues, we propose a novel SAM-guided fusion network (SAM-FNet), a dual-branch network for laryngo-pharyngeal tumor detection. By leveraging the powerful object segmentation capabilities of the Segment Anything Model (SAM), we introduce the SAM into the SAM-FNet to accurately segment the lesion region. Furthermore, we propose a GAN-like feature optimization (GFO) module to capture the discriminative features between the global and local branches, enhancing the fusion feature complementarity. Additionally, we collect two LPC datasets from the First Affiliated Hospital (FAHSYSU) and the Sixth Affiliated Hospital (SAHSYSU) of Sun Yat-sen University. The FAHSYSU dataset is used as the internal dataset for training the model, while the SAHSYSU dataset is used as the external dataset for evaluating the model's performance. Extensive experiments on both datasets of FAHSYSU and SAHSYSU demonstrate that the SAM-FNet can achieve competitive results, outperforming the state-of-the-art counterparts. The source code of SAM-FNet is available at the URL of https://github.com/VVJia/SAM-FNet.<|reference_end|>
@article{wei2024sam-fnet:,
    title={SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor
  Detection},
    author={Jia Wei, Yun Li, Meiyu Qiu, Hongyu Chen, Xiaomao Fan, Wenbin Lei},
    journal={arXiv preprint arXiv:2408.05426},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05426},
    primaryClass={cs.CV}
}