强大的双流诊断网络用于超声波乳腺瘤分类与跨域细分Priors
Xiaokai Jiang1, Xuewen Ding1, Jinying Ma1
1School of Electronic Engineering, Tianjin University of Technology and Education, Tianjin 300222, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
一个新的双流诊断网络 (DSDNet) 从超声波图像改进了乳腺癌诊断. 这种方法通过使用细分先验来指导分类来提高准确性,克服噪音和可变图像质量等挑战,以便更好地早期检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 超声波成像对于早期发现乳腺癌至关重要,但由于噪音,低对比度和解剖复杂性,图像的解释是困难的.
- 超声波图像在不同环境中的变化阻碍了可靠的计算机辅助诊断系统的开发.
研究的目的:
- 为超声波成像开发一个强大的乳腺瘤诊断策略,克服当前的解释挑战.
- 引入一个新的双流诊断网络 (DSDNet) 进行准确和稳定的乳腺癌分类.
主要方法:
- 使用双流诊断网络 (DSDNet) 进行跨域细分的先前指导分类策略.
- DSDNet具有结分段分支,为分类骨干提供空间前置.
- 新型模块包括双流面罩注意力 (DSMA),分段先导融合 (SPGF) 和Mamba启发的线性变压器 (MILT) 块.
主要成果:
- 在基准数据集上,DSDNet实现了高精度 (ACC):0.878 (BUSI),0.836 (BUS) 和0.882 (GDPH_SYSUCC).
- 获得了优异的回忆分数:0.866 (BUSI),0.789 (BUS) 和0.878 (GDPH_SYSUCC). 这是一个非常好的回忆分数.
- 拟议的方法在各种成像条件下表现出稳定有效的性能.
结论:
- 在超声波成像中,DSDNet框架为乳腺瘤诊断提供了强大的和有效的解决方案.
- 跨领域细分预先指导策略显著提高了分类性能和通用性.
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