Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image
  Translation for Histopathology Images
Zhiyun Song Penghui Du Junpeng Yan Kailu Li Jianzhong Shou Maode Lai Yubo Fan Yan Xu
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.