半监督细分组织病理学图像与噪音意识的拓一致性
Meilong Xu1, Xiaoling Hu2, Saumya Gupta1
1Stony Brook University, Stony Brook, NY, USA.
Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision
|June 25, 2025
概括
TopoSemiSeg推出了一种新的半监督学习方法,用于数字病理图像细分. 它从未标记的数据中准确地捕捉了腺体和细胞核拓,克服了现有方法中常见的错误.
科学领域:
- 数字病理学数字病理学
- 医学图像分析 医学图像分析
- 计算机视觉 计算机视觉
背景情况:
- 密集的结构如腺体和细胞核的精确细分对于数字病理学分析至关重要.
- 手动的像素智能注释是劳动密集型的,需要对细分任务进行半监督的方法.
- 当前的半监督方法经常产生拓不准确性,例如合并或缺失的结构.
研究的目的:
- 开发了第一个半监督细分方法,TopoSemiSeg,能够从未标记的基因病理图像中学习拓表示.
- 为了应对半监督学习中从未标记的数据中杂的拓预测的挑战.
- 为了提高对拓错误的细分模型的稳定性和准确性.
主要方法:
- 提出了TopoSemiSeg,这是一个新的半监督方法,用于组织病理学图像细分.
- 引入了噪声感知拓一致性损失,以对齐教师和学生模型表示.
- 将预测拓分解为信号和噪声组件,以学习真正的拓信号并增强噪声强度.
主要成果:
- 与现有方法相比,TopoSemiSeg在公共基因病理图像数据集上表现出卓越的性能.
- 取得了显著的改进,特别是在拓意识评估指标上.
- 验证了该方法在从未标记的数据中学习准确的拓表示的有效性.
结论:
- TopoSemiSeg代表了数字病理学半监督细分的重大进展.
- 建议的噪声感知拓一致性损失有效地减轻了未标记数据中的拓错误.
- 该方法显示了通过提供准确,拓意识的细分来改善数字病理学的下游分析的巨大潜力.
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