全局对比掩码自编码器是强大的病理表征学习者
Global Contrast Masked Autoencoders Are Powerful Pathological Representation Learners
AI总结：
针对病理图像标注困难、数据缺乏的问题，提出全局对比掩码自编码器（GCMAE）自监督模型，学习局部-全局特征并提升跨数据集迁移性能，在三个HE染色病理数据集上验证有效性，并设计了自动化病理诊断流程。
摘要：
基于数字病理切片扫描技术，以深度学习为代表的人工智能算法在计算病理学领域取得了显著成果。与其他医学图像相比，病理图像更难标注，因此，用于进行监督学习以训练稳健深度学习模型的可用数据集极度缺乏。在本文中，我们提出了一种自监督学习（SSL）模型，即全局对比掩码自编码器（GCMAE），该模型能够训练编码器具备表征病理图像局部-全局特征的能力，同时也显著提高了跨数据集的迁移学习性能。在本研究中，通过使用总共三个不同的疾病特异性苏木精-伊红（HE）染色病理数据集：Camelyon16、NCTCRC和BreakHis，进行了大量实验，证明了GCMAE学习可迁移表征的能力。此外，本研究基于GCMAE设计了一种有效的自动化病理诊断流程，用于临床应用。本文的源代码公开于https://github.com/StarUniversus/gcmae。
英文摘要：
Based on digital pathology slice scanning technology, artificial intelligence algorithms represented by deep learning have achieved remarkable results in the field of computational pathology. Compared to other medical images, pathology images are more difficult to annotate, and thus, there is an extreme lack of available datasets for conducting supervised learning to train robust deep learning models. In this paper, we propose a self-supervised learning (SSL) model, the global contrast-masked autoencoder (GCMAE), which can train the encoder to have the ability to represent local-global features of pathological images, also significantly improve the performance of transfer learning across data sets. In this study, the ability of the GCMAE to learn migratable representations was demonstrated through extensive experiments using a total of three different disease-specific hematoxylin and eosin (HE)-stained pathology datasets: Camelyon16, NCTCRC and BreakHis. In addition, this study designed an effective automated pathology diagnosis process based on the GCMAE for clinical applications. The source code of this paper is publicly available at https://github.com/StarUniversus/gcmae.
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。
