Berkeley Single-Cell Computational Microscopy (BSCCM) dataset

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 400,000 images 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.