  Pathological image analysis is a crucial field in computer-aided diagnosis,
where deep learning is widely applied. Transfer learning using pre-trained
models initialized on natural images has effectively improved the downstream
pathological performance. However, the lack of sophisticated domain-specific
pathological initialization hinders their potential. Self-supervised learning
(SSL) enables pre-training without sample-level labels, which has great
potential to overcome the challenge of expensive annotations. Thus, studies
focusing on pathological SSL pre-training call for a comprehensive and
standardized dataset, similar to the ImageNet in computer vision. This paper
presents the comprehensive pathological image analysis (CPIA) dataset, a
large-scale SSL pre-training dataset combining 103 open-source datasets with
extensive standardization. The CPIA dataset contains 21,427,877 standardized
images, covering over 48 organs/tissues and about 100 kinds of diseases, which
includes two main data types: whole slide images (WSIs) and characteristic
regions of interest (ROIs). A four-scale WSI standardization process is
proposed based on the uniform resolution in microns per pixel (MPP), while the
ROIs are divided into three scales artificially. This multi-scale dataset is
built with the diagnosis habits under the supervision of experienced senior
pathologists. The CPIA dataset facilitates a comprehensive pathological
understanding and enables pattern discovery explorations. Additionally, to
launch the CPIA dataset, several state-of-the-art (SOTA) baselines of SSL
pre-training and downstream evaluation are specially conducted. The CPIA
dataset along with baselines is available at
https://github.com/zhanglab2021/CPIA_Dataset.
