乳房超声波图像分割使用多分支跳过连接搜索
Yue Wu1, Lin Huang2, Tiejun Yang3
1College of Intelligent Medicine and Biotechnology, Guilin Medical University, Guilin, 541199, Guangxi, China.
Journal of imaging informatics in medicine
|April 2, 2025
概括
本研究引入了一种自动化方法,用于设计乳房超声波细分神经网络,实现高精度和降低诊断风险. 这种新的方法有效地找到最佳模型,改善病变边界定位,以便更好地规划治疗.
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:
- 精确的乳房超声波图像细分对于早期癌症检测和治疗计划至关重要.
- 目前用于细分的神经网络设计是昂贵和耗时的.
- 提高细分精度可以减少错误诊断,帮助个性化医疗.
研究的目的:
- 开发一种用于乳房超声波图像细分的自动神经网络架构搜索方法.
- 为了降低成本并提高细分模型设计的准确性.
- 增强病变边界的局部化,以支持临床决策.
主要方法:
- 提出了一个新的编码器-解码器神经网络架构搜索方法.
- 引入了多分支跳过连接模块,具有独特的特征提取操作.
- 采用可学习的操作重量搜索策略,使用Gumbel-Softmax进行优化.
- 在候选模型中集成Swin变压器和卷积块.
主要成果:
- 在大约两个小时内确定了最佳的编码器解码器模型.
- 实现了优异的细分精度,子得分为~85.94% (BUSI) 和~84.44% (OASBUD).
- 超越了包括AAU-Net,SK-U-Net和TransUNet在内的最先进的方法.
结论:
- 自动化方法有效设计高性能细分模型.
- 卓越的准确性增强了病变边界定位,减少了诊断错误.
- 来自细分的定量指标可以支持个性化治疗规划.
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