类似性引导的模型群:在计算病理学中增强半监督学习
Zhilong Weng1, Alexey Pryalukhin2, Wolfgang Hulla2
1Institute of Pathology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
Scientific reports
|December 30, 2025
概括
本研究介绍了一种Swarm-of-Models (S-o-M) 框架,用于计算病理学中的半监督学习 (SSL). 它通过利用字母之间的相似性来提高像素级注释准确性,以获得更可靠的伪标签.
科学领域:
- 计算病理学计算病理学
- 数字病理学数字病理学
- 机器学习在医学中的应用
背景情况:
- 高精度的像素级注释是计算病理学的重要瓶,需要大量的时间和专家输入.
- 半监督学习 (SSL) 通过利用未标记的数据提供了一个解决方案,但现有的方法往往忽视了关键的案例间相似性,以便准确的伪标签.
研究的目的:
- 引入一种新的Swarm-of-Models (S-o-M) SSL框架,旨在提高语义细分任务的伪标签可靠性.
- 通过结合案例相似之处来提高计算病理学模型的准确性和效率.
主要方法:
- 开发了一个Swarm-of-Models (S-o-M) SSL框架,可以根据图像相似性动态选择专门的"形态学专家"模型.
- 将框架应用于整个幻灯片图像 (WSIs) 进行语义细分,重点是改进伪标签生成.
主要成果:
- 与传统的监督和半监督方法相比,S-o-M框架在一个大型的国际结直肠数据集上表现出更高的性能.
- 在Dice得分中取得了改进:瘤细分为3.6%,瘤/瘤侧膜细分为2.1%.
- 废弃性研究证实了该框架在不同数据条件和单心训练场景中的稳定性.
结论:
- 拟议的S-o-M SSL框架有效地利用案例间的相似性,以提高计算病理学中的伪标签准确性.
- 纳入特定病例的相似性对于开发更有效和更可概括的计算病理学模型至关重要.
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