Computational microscopy, in which hardware and algorithms of an imaging system are jointly designed, shows promise for making imaging systems that cost less, perform more robustly, and collect new types of information. Often, the performance of computational imaging systems, especially those that incorporate machine learning, is sample-dependent. Thus, standardized datasets are an essential tool for comparing the performance of different approaches. Here, we introduce the Berkeley Single Cell Computational Microscopy (BSCCM) dataset, which contains over ~12,000,000 images of 400,000 of individual white blood cells. The dataset contains images captured with multiple illumination patterns on an LED array microscope and fluorescent measurements of the abundance of surface proteins that mark different cell types. We hope this dataset will provide a valuable resource for the development and testing of new algorithms in computational microscopy and computer vision with practical biomedical applications.

计算显微镜学，硬件和算法的联合设计，对于制造成本更低、性能更强大、收集新类型信息的成像系统有很大潜力。通过引入伯克利单个细胞计算显微学（BSCCM）数据集，包含超过12,000,000张400,000个单个白细胞图像，采用LED阵列显微镜多种照明模式捕获图像以及标记不同细胞类型的表面蛋白质的荧光测量，希望该数据集能为计算显微学和具有实际生物医学应用的计算机视觉算法的开发与测试提供宝贵资源。

伯克利单细胞计算显微（BSCCM）数据集