论文标题
组织病理学图像分析的染色自适应自我监督学习
Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis
论文作者
论文摘要
人们普遍认为，污渍差异引起的颜色变化是组织病理学图像分析的关键问题。现有方法采用颜色匹配，染色分离，污渍转移或它们的组合以减轻污渍变化问题。在本文中，我们提出了一种用于组织病理学图像分析的新型染色自适应自我监督学习（SASSL）方法。我们的SASSL将一个域 - 交流训练模块集成到SSL框架中，以学习独特的特征，这些功能对各种转换和污渍变化都具有鲁棒性。所提出的SASSL被视为域不变特征提取的一般方法，可以通过对特定下游任务的特定特征进行细微调整特征，从而将其与任意下游组织病理学图像分析模块（例如核/组织分割）一起灵活结合。我们进行了有关公开可用的病理图像分析数据集的实验，包括熊猫，bresspathq和camelyon16数据集，以实现最先进的性能。实验结果表明，所提出的方法可以鲁棒地提高模型的特征提取能力，并在下游任务中实现稳定的性能改善。
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.
