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.

翻译：计算显微镜通过联合设计成像系统的硬件与算法，有望开发出成本更低、性能更稳健且能采集新型信息的成像系统。计算成像系统（尤其是融合机器学习的系统）的性能往往依赖于样本特性。因此，标准化数据集是评估不同方法性能的关键工具。本文介绍了伯克利单细胞计算显微镜(BSCCM)数据集，该数据集包含约40万个单个白细胞的超过1200万张图像。这些图像采用LED阵列显微镜的多种照明模式采集，并包含标记不同细胞类型的表面蛋白丰度的荧光测量值。我们期望该数据集能为计算显微镜与计算机视觉领域新算法（具有实际生物医学应用潜力）的研发与测试提供宝贵资源。