用于组织病理学图像分类的自蒸馏增强掩码自编码器
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification
AI总结：
本文提出自蒸馏增强掩码自编码器SD-MAE，通过在可见图像块上引入自蒸馏损失提升浅层编码器表示能力，并在组织病理图像分类、细胞分割和目标检测中验证其竞争力。
摘要：
近年来，自监督学习（SSL）在组织病理学图像分析中受到越来越多的关注。与受假阴性问题困扰的对比学习（即语义相似的图像被选为负样本）相比，基于生成范式构建自监督学习的掩码自编码器（MAE）可能是更合适的预训练方法。在本文中，我们引入MAE并验证可见图像块对组织病理学图像理解的作用。此外，我们提出了一种新颖的SD-MAE模型，以实现自蒸馏增强的MAE。除了对掩码图像块的重建损失外，SD-MAE还对可见图像块施加自蒸馏损失，以增强位于浅层编码器的表示能力。我们将SD-MAE应用于组织病理学图像分类、细胞分割和目标检测。实验表明，在这些任务中，SD-MAE与其他自监督学习方法相比表现出极具竞争力的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
