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LoCo: Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation

Autor: Cai, Lingcong, Li, Yun, Fan, Xiaomao, Song, Kaixuan, Li, Yongcheng, Yuan, Yixuan, Wang, Ruxin, Lei, Wenbin

The segmentation of endoscopic images plays a vital role in computer-aided diagnosis and treatment. The advancements in deep learning have led to the employment of numerous models for endoscopic tumor segmentation, achieving promising segmentation pe

Externí odkaz: http://arxiv.org/abs/2412.02314

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SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection

Autor: Wei, Jia, Li, Yun, Fan, Xiaomao, Ma, Wenjun, Qiu, Meiyu, Chen, Hongyu, Lei, Wenbin

Laryngo-pharyngeal cancer (LPC) is a highly lethal malignancy in the head and neck region. Recent advancements in tumor detection, particularly through dual-branch network architectures, have significantly improved diagnostic accuracy by integrating

Externí odkaz: http://arxiv.org/abs/2410.21813

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3D-LSPTM: An Automatic Framework with 3D-Large-Scale Pretrained Model for Laryngeal Cancer Detection Using Laryngoscopic Videos

Autor: Qiu, Meiyu, Li, Yun, Huang, Wenjun, Zhang, Haoyun, Zheng, Weiping, Lei, Wenbin, Fan, Xiaomao

Laryngeal cancer is a malignant disease with a high morality rate in otorhinolaryngology, posing an significant threat to human health. Traditionally larygologists manually visual-inspect laryngeal cancer in laryngoscopic videos, which is quite time-

Externí odkaz: http://arxiv.org/abs/2409.01459

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SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection

Autor: Wei, Jia, Li, Yun, Qiu, Meiyu, Chen, Hongyu, Fan, Xiaomao, Lei, Wenbin

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 advancemen

Externí odkaz: http://arxiv.org/abs/2408.05426

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Rethinking Radiology Report Generation via Causal Inspired Counterfactual Augmentation

Autor: Song, Xiao, Liu, Jiafan, Li, Yun, Liu, Yan, Lei, Wenbin, Wang, Ruxin

Radiology Report Generation (RRG) draws attention as a vision-and-language interaction of biomedical fields. Previous works inherited the ideology of traditional language generation tasks, aiming to generate paragraphs with high readability as report

Externí odkaz: http://arxiv.org/abs/2311.13307

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