Glomerulus in multiple stains obtained via large-scale web image mining

We collected over 10,000 compound figures through the NIH Open-i search engine with the keywords “glomerular OR glomeruli OR glomerulus”. Then we used our compound figure separation network to process all compound images to get over 36,000 glomeruli pathologies obtained by different microscopy or in different stains with a confidence threshold of 0.7. We split the data into 3 classes by different microscopy which are light microscopy, electron microscopy, and fluorescent.

本研究通过美国国立卫生研究院（National Institutes of Health, NIH）的Open-i搜索引擎，以"glomerular OR glomeruli OR glomerulus"为关键词进行检索，共收集到10000余张复合插图。随后使用自研的复合图像分离网络处理所有复合图像，以0.7的置信度阈值完成筛选，最终得到36000余张来源于不同显微镜技术或不同染色方式的肾小球病理图像。本数据集按照所用显微镜类型划分为三类，分别为光学显微镜、电子显微镜及荧光显微镜。