论文标题
多模式蒙版自动编码器学习组成组织病理学表示
Multi-modal Masked Autoencoders Learn Compositional Histopathological Representations
论文作者
论文摘要
自我监督学习（SSL）通过利用不需要标签的借口任务来学习有用的归纳偏见。 SSL的未标记性质使得对整个幻灯片组织病理学图像（WSI）尤为重要，在该图片级的人类注释很难。蒙版自动编码器（MAE）是一种适合数字病理的SSL方法，因为它不需要负面采样，并且几乎不需要数据增加。但是，自然图像和数字病理图像之间的域移动需要进一步研究贴片级WSIS的MAE。在本文中，我们研究了MAE在组织病理学中的几种设计选择。此外，我们引入了一个多模式MAE（MMAE），该MAE（MMAE）利用了苏木精和曙红（H＆E）染色的WSI的特定组成性。我们在公共补丁级数据集NCT-CRC-HE-100K上进行了实验。结果表明，对于八类组织表型任务，MMAE架构的表现优于监督基线和其他最先进的SSL技术，仅利用100个标记的样品进行微调。我们的代码可在https://github.com/wisdomikezogwo/mmae_pathology获得
Self-supervised learning (SSL) enables learning useful inductive biases through utilizing pretext tasks that require no labels. The unlabeled nature of SSL makes it especially important for whole slide histopathological images (WSIs), where patch-level human annotation is difficult. Masked Autoencoders (MAE) is a recent SSL method suitable for digital pathology as it does not require negative sampling and requires little to no data augmentations. However, the domain shift between natural images and digital pathology images requires further research in designing MAE for patch-level WSIs. In this paper, we investigate several design choices for MAE in histopathology. Furthermore, we introduce a multi-modal MAE (MMAE) that leverages the specific compositionality of Hematoxylin & Eosin (H&E) stained WSIs. We performed our experiments on the public patch-level dataset NCT-CRC-HE-100K. The results show that the MMAE architecture outperforms supervised baselines and other state-of-the-art SSL techniques for an eight-class tissue phenotyping task, utilizing only 100 labeled samples for fine-tuning. Our code is available at https://github.com/wisdomikezogwo/MMAE_Pathology
