Detectingandsegmentingobjectswithinwholeslideimagesis essential in computational pathology workflow. Self-supervised learning (SSL) is appealing to such annotation-heavy tasks. Despite the extensive benchmarks in natural images for dense tasks, such studies are, unfortunately, absent in current works for pathology. Our paper intends to narrow this gap. We first benchmark representative SSL methods for dense prediction tasks in pathology images. Then, we propose concept contrastive learning (ConCL), an SSL framework for dense pre-training. We explore how ConCL performs with concepts provided by different sources and end up with proposing a simple dependency-free concept generating method that does not rely on external segmentation algorithms or saliency detection models. Extensive experiments demonstrate the superiority of ConCL over previous state-of-the-art SSL methods across different settings. Along our exploration, we distll several important and intriguing components contributing to the success of dense pre-training for pathology images. We hope this work could provide useful data points and encourage the community to conduct ConCL pre-training for problems of interest. Code is available.

本文旨在填补计算病理学领域中，目标检测和分割缺乏自监督学习方法的空白。我们基于自监督学习提出了一种概念对比学习方法，通过实验验证在不依赖外部算法和模型的情况下，相较于其他方法在不同设置下更为优秀。我们总结了此方法的核心组成部分，提供了有用的数据点，并鼓励其他研究者探索类似的预训练方法。

ConCL: 病理图像中密集预测预训练的概念对比学习