Local representation learning has been a key challenge to promote the performance of the histopathological whole slide images analysis. The previous representation learning methods followed the supervised learning paradigm. However, manual annotation for large-scale WSIs is time-consuming and labor-intensive. Hence, the self-supervised contrastive learning has recently attracted intensive attention. The present contrastive learning methods treat each sample as a single class, which suffers from class collision problems, especially in the domain of histopathology image analysis. In this paper, we proposed a novel contrastive representation learning framework named Lesion-Aware Contrastive Learning (LACL) for histopathology whole slide image analysis. We built a lesion queue based on the memory bank structure to store the representations of different classes of WSIs, which allowed the contrastive model to selectively define the negative pairs during the training. Moreover, We designed a queue refinement strategy to purify the representations stored in the lesion queue. The experimental results demonstrate that LACL achieves the best performance in histopathology image representation learning on different datasets, and outperforms state-of-the-art methods under different WSI classification benchmarks. The code is available at https://github.com/junl21/lacl.
翻译:当地代表制学习是推广全幻灯片病理学图像分析工作的关键挑战。以前的代表制学习方法遵循了受监督的学习模式。然而,大规模世界科学倡议的人工批注耗时费力。因此,自我监督的对比学习最近引起人们的高度关注。目前的对比式学习方法将每个样本都作为单类处理,它们都存在阶级碰撞问题,特别是在组织病理学图像分析领域。在本文中,我们提议了一个新的对比性代表性学习框架,名为“Lesion-Aware Contrasive Lesion(LACL)”,用于进行整个幻灯片病理学图像分析。我们根据记忆库结构建立了一个“分级列队列”,以储存不同类别世界科学倡议的表述,从而使得对比性模型能够在培训期间有选择地界定负对子。此外,我们设计了一个队列精细化战略,以净化储存在古迹队列中的演示。实验结果显示,LACLS在他病理学图像表现方面取得最佳的成绩,在不同的数据集中学习,以及超越州/州/州图理学方法,在不同的WSI分类分类下,可以使用不同的标准。