VMDU-net:一个双编码器的多尺度融合网络,用于聚细分,与Vision Mamba和交叉形状变压器集成
Peng Li1, Jianhua Ding2, Chia S Lim1
1School of Computing & Technology, Asia Pacific University of Technology & Innovation, Lebuhraya Bukit Jalil, Taman Teknologi Malaysia, Bukit Jalil, Kuala Lumpur, Malaysia.
Frontiers in artificial intelligence
|July 3, 2025
概括
一个新的双编码器网络VMDU-Net,改进了用于早期结直肠癌检测的聚细分. 它有效地解决了多形状,边界和大小变化的挑战,增强了临床应用.
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:
- 结肠直肠癌通常来自多,需要早期检测和切除.
- 精确的聚细分对于预防癌症进展至关重要,但面临着诸如多样化的聚形态和不清楚的边界等挑战.
- 现有的细分算法在长距离的依赖性和有效的融合方面扎.
研究的目的:
- 引入VMDU-Net,这是一种新的双编码器多尺度特征融合网络,用于增强多片细分.
- 克服当前的多片细分方法的局限性,特别是处理复杂的多片特征和改善融合.
- 通过改进的多重体鉴定,推进结直肠癌的早期检测和预防.
主要方法:
- 开发了VMDU-Net,配备了Vision Mamba和交叉形状变压器模块的并行编码器.
- 集成了一个Mamba-Transformer-Merge (MTM) 模块,用于注意力加权的空间和通道特征的融合.
- 采用深度可分离的卷积,用于多尺度的特征提取和改进的融合效率.
主要成果:
- VMDU-Net在五个多片细分数据集上表现出优于最先进的方法的性能.
- 在Kvasir-SEG上获得了0.934分,在CVC-ClinicDB上获得了0.951分,这表明了优异的细分精度.
- 在保存多的边界细节方面表现出显著的改进.
结论:
- 通过整合Mamba和Transformer架构,VMDU-Net有效地解决了聚细分的关键挑战.
- 该网络在各种数据集中的强大性能表明,在早期结直肠癌预防中,临床应用的潜力很大.
- 拟议的方法提高了细分的准确性和边界的保护,这对于有效的多片去除至关重要.
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