ImmunoAIzer: A deep-learning-based computational framework to characterize cell distribution and gene mutation in tumor microenvironment. Chang Bian et al.

This dataset contains the original unprocessed H&E and mIHC image slides of the paper:"ImmunoAIzer: A deep-learning-based computational framework to characterize cell distribution and gene mutation in tumor microenvironment". The original data of the paper contains 8 H&E WSIs and their corresponding mIHC WSIs. The slides are stored in qptiff format, which can be viewed by phenochart software or any other WSI viewing softwares. Because the original file size is about 30GB after compressing and the available storage of mendeley data project is 10GB, we only uploaded the H&E WSI and its corresponding mIHC WSI of 1 tissue sample. The whole original dataset can be accessed by reasonable request.

本数据集包含论文《ImmunoAIzer：一种用于表征肿瘤微环境中细胞分布与基因突变的深度学习计算框架》中未经过预处理的原始苏木精-伊红（Hematoxylin-Eosin, H&E）染色与多重免疫组化（multiplex immunohistochemistry, mIHC）病理图像切片。该论文的原始数据集包含8张全视野数字切片（Whole Slide Image, WSI）格式的H&E染色图像，以及与之对应的mIHC染色全视野数字切片。上述图像切片以qptiff格式存储，可通过phenochart软件或其他任意全视野数字切片查看软件进行浏览。由于原始数据集压缩后总大小约为30GB，而Mendeley数据项目的可用存储空间仅为10GB，因此本次仅上传了1份组织样本对应的H&E染色全视野数字切片及其匹配的mIHC染色全视野数字切片。完整的原始数据集可通过合理申请获取。