Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images. Despite its success, few works concentrate on the extraction of nucleus-level information, which is essential for pathologic analysis. In this work, we propose a novel nucleus-aware self-supervised pretraining framework for histopathology images. The framework aims to capture the nuclear morphology and distribution information through unpaired image-to-image translation between histopathology images and pseudo mask images. The generation process is modulated by both conditional and stochastic style representations, ensuring the reality and diversity of the generated histopathology images for pretraining. Further, an instance segmentation guided strategy is employed to capture instance-level information. The experiments on 7 datasets show that the proposed pretraining method outperforms supervised ones on Kather classification, multiple instance learning, and 5 dense-prediction tasks with the transfer learning protocol, and yields superior results than other self-supervised approaches on 8 semi-supervised tasks. Our project is publicly available at https://github.com/zhiyuns/UNITPathSSL.

提出一种新颖的基于细胞核感知的自监督预训练框架，通过组织病理图像与伪掩膜图像之间的无配对图像转换来捕获细胞核形态和分布信息，并采用条件和随机样式表示来调节生成过程，以实现预训练图像的真实性和多样性；同时，采用实例分割引导策略来捕获实例级信息。实验结果表明，该预训练方法在Kather分类、多实例学习和5个密集预测任务上优于有监督方法，并且在8个半监督任务上比其他自监督方法取得了更好的结果。

基于核心感知的自监督预训练：使用图像对图像转换技术的非配对组织病理学图像