CoF-DResNet:基于动态协调代谢注意力和结构注意力的癌症转移识别网络
Sun Zhu1, Huiyan Jiang1,2, Zhaoshuo Diao1
1Software College, Northeastern University, Shenyang 110169, China.
Current pharmaceutical biotechnology
|June 26, 2024
概括
这项研究引入了一个新的网络,用于识别癌症转移,使用组合的代谢和结构成像数据. 该模型实现了更高的准确性,提高了用于先进癌症检测的诊断能力.
科学领域:
- 医疗成像医学成像
- 在瘤学中使用人工智能
- 癌症的诊断 癌症的诊断
背景情况:
- 癌症转移的识别对于患者的预后和治疗计划至关重要.
- F-FDG PET/CT提供了用于癌症诊断的综合代谢和结构成像.
- 整合代谢和结构信息是改善转移识别的关键.
研究的目的:
- 提出一个新的癌症转移识别网络.
- 通过整合代谢和结构信息来增强特征表示和识别性能.
- 通过多受体场融合来提高特征表达的有效性.
主要方法:
- 一个癌症转移识别网络,利用动态协调代谢注意力和结构性注意力.
- 将动态协调注意模块 (DCAM) 纳入ResNet分支,以提取代谢和结构特征.
- 使用多感应场特征融合模块 (MRFM) 进行语义特征融合.
主要成果:
- 在私人肺淋巴结和公共软组织肉瘤数据集上进行了实验.
- 拟议的方法在肺淋巴结数据集上实现了76.0%的准确性.
- 拟议的方法在软组织肉瘤数据集上实现了75.1%的准确性.
结论:
- 与标准ResNet相比,拟议网络的准确性有所提高,在各自的数据集上分别增加了6.8%和5.6%.
- 集成动态协调注意力和多受体场特征融合可以提高癌症转移的识别.
- 这项研究证实了拟议的方法对晚期癌症诊断成像的有效性.
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