PGMNet:基于比特平面切片和多尺度自适应融合的多片细分网络
Dong Wang1, Shan Lin Liu1, Shuai Li1
1School of Computer Science and Engineering, Chongqing University of Technology, Hongguang Avenue, Banan District, Chongqing 400054, People's Republic of China.
Biomedical physics & engineering express
|December 22, 2025
概括
一个新的深度学习模型,PGMNet,增强了在结肠镜检查期间的聚细分,以预防结肠直肠癌. 这种精确高效的网络可以改善多体的检测和细分,帮助早期诊断和治疗.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 在结肠镜检查中准确检测和细分多,对于早期结肠直肠癌的预防和治疗至关重要.
- 聚体大小,形状和模糊边界的变化对当前的深度学习 (DL) 分段方法构成重大挑战,导致不稳定和不满意的结果.
研究的目的:
- 开发一个准确和高效的深度学习网络,PGMNet,以改善结肠镜图像中的多片细分.
- 解决现有的DL方法在处理多变异和边界模糊性方面的局限性.
主要方法:
- PGMNet使用PVTv2编码器来捕获微细细节和全球语义信息.
- 该网络包含一个全球-本地交互关系模块 (GLIRM),用于多层次信息融合和噪声抑制.
- 带有门机制的多级特征聚合模块 (MFAM) 有效地聚合特征,以提高预测质量.
主要成果:
- 在五个公开的多数据集中,PGMNet表现出有希望的性能,显示出强大的细分精度和概括能力.
- 在具有挑战性的ETIS数据集中,PGMNet实现了平均Dice系数 (mDice) 的82.33%和平均交叉在Union (mIoU) 的74.29%.
结论:
- 与现有方法相比,PGMNet为聚细分提供了优越的解决方案.
- 拟议的网络显示了通过加强结肠镜聚检测和细分来改善早期结肠直肠癌诊断的巨大潜力.
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