It is commonly recognized that color variations caused by differences in stains is a critical issue for histopathology image analysis. Existing methods adopt color matching, stain separation, stain transfer or the combination of them to alleviate the stain variation problem. In this paper, we propose a novel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathology image analysis. Our SASSL integrates a domain-adversarial training module into the SSL framework to learn distinctive features that are robust to both various transformations and stain variations. The proposed SASSL is regarded as a general method for domain-invariant feature extraction which can be flexibly combined with arbitrary downstream histopathology image analysis modules (e.g. nuclei/tissue segmentation) by fine-tuning the features for specific downstream tasks. We conducted experiments on publicly available pathological image analysis datasets including the PANDA, BreastPathQ, and CAMELYON16 datasets, achieving the state-of-the-art performance. Experimental results demonstrate that the proposed method can robustly improve the feature extraction ability of the model, and achieve stable performance improvement in downstream tasks.
翻译:人们普遍认识到,污点差异造成的颜色差异是组织病理图象分析的一个关键问题。现有的方法采用色比、污点分离、污点转移或结合等方法来缓解污点变异问题。在本文件中,我们建议采用新型的Stain-Adapitive自增强学习系统(SASSL)方法来进行组织病理图象分析。我们的SASL将一个域对立培训模块纳入SSL框架,以了解各种变异和污点变异的特征。拟议的SASSL被视为一种一般的域变异特性提取方法,可以与任意下游的病理病理图象分析模块(例如核/质分解)灵活地结合起来,对具体的下游任务特征进行微调。我们用公开的病理图象分析数据集进行了实验,包括PANDA、CroupPathQ和CAMELYON16数据集,从而实现最新性能。实验结果表明,拟议的方法可以有力地提高模型的特征提取能力,并实现下游任务的稳定性改进。