Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years. Compared to contrastive learning which is troubled with the false negative problem, i.e., semantically similar images are selected as negative samples, masked autoencoders (MAE) building SSL from a generative paradigm is probably a more appropriate pre-training. In this paper, we introduce MAE and verify the effect of visible patches for histopathological image understanding. Moreover, a novel SD-MAE model is proposed to enable a self-distillation augmented MAE. Besides the reconstruction loss on masked image patches, SD-MAE further imposes the self-distillation loss on visible patches to enhance the representational capacity of the encoder located shallow layer. We apply SD-MAE to histopathological image classification, cell segmentation and object detection. Experiments demonstrate that SD-MAE shows highly competitive performance when compared with other SSL methods in these tasks.

翻译：自监督学习（SSL）近年来在组织病理图像分析中受到越来越多的关注。相较于受假负样本问题（即选择语义相似的图像作为负样本）困扰的对比学习，基于生成范式构建自监督学习的掩码自编码器（MAE）可能是一种更合适的预训练方法。本文引入MAE并验证了可见块在组织病理图像理解中的作用。此外，我们提出了新颖的SD-MAE模型，实现了自蒸馏增强的MAE。除对掩码图像块施加重构损失外，SD-MAE进一步在可见块上施加自蒸馏损失，以增强浅层编码器的表示能力。我们将SD-MAE应用于组织病理图像分类、细胞分割和物体检测任务。实验表明，与这些任务中的其他自监督学习方法相比，SD-MAE表现出极具竞争力的性能。