MMNet:一个混合模块网络用于聚体细分
Raman Ghimire1, Sang-Woong Lee2
1Pattern Recognition and Machine Learning Lab, Department of IT Convergence Engineering, Gachon University, Seongnam 13557, Republic of Korea.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种混合变压器和卷积混合网络 (MMNet),以改进多重体细分. MMNet有效地捕捉了远程依赖,同时降低了计算成本,优于现有方法.
科学领域:
- 医学图像分析 医学图像分析
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 传统的编码器-解码器网络 (例如,U-Net) 在聚细分中与远程依赖性作斗争,优先考虑本地模式而不是全球背景.
- 变压器网络擅长通过自我注意来捕捉远程依赖,但由于图像大小的二次复杂性而面临计算挑战.
- 现有的变压器方法缺乏诱导偏差,由于有限的低级特征提取,阻碍了对局部环境的概括.
研究的目的:
- 开发一种新的混合网络,以解决现有的聚细分方法的局限性.
- 改进远程依赖的建模,同时减轻与变压器架构相关的计算成本.
- 通过整合卷积感应偏差来增强细分模型的概括能力.
主要方法:
- 引入了一个混合变压器与卷积混合网络 (MMNet) 结合.
- 使用预训练过的变压器作为特征提取编码器.
- 开发了一种混合模块网络 (MMNet),使用深度和1x1卷积来有效地进行远程依赖模型 (空间和跨通道相关性).
主要成果:
- 拟议的MMNet有效地捕获了远程依赖,并减少了计算开销.
- 对五个多数据集的定性和定量评估表明了卓越的性能.
- 在6个指标上,MMNet的表现优于以前的最先进的多体细分方法.
结论:
- 混合MMNet架构为聚细分提供了有效的解决方案,平衡了远程依赖模型与计算效率.
- 这种方法克服了纯粹卷积或基于变压器的方法的局限性.
- MMNet代表了医疗成像应用程序的自动化聚细分的重大进步.
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