# The Berkeley Single Cell Computational Microscopy (BSCCM) Dataset

arXiv: https://arxiv.org/abs/2402.06191 · HTML: https://mlsanity.com/paper/2402.06191/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging](https://mlsanity.com/paper/1808.03571/) (2018)
- [Convolutional neural networks that teach microscopes how to image](https://mlsanity.com/paper/1709.07223/) (2017)
- [Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico](https://mlsanity.com/paper/1711.06323/) (2017)
- [High-throughput intensity diffraction tomography with a computational microscope](https://mlsanity.com/paper/1801.09773/) (2018)
- [Efficient illumination angle self-calibration in Fourier ptychography](https://mlsanity.com/paper/1804.03299/) (2018)

## Built on by (most cited first, 2 of 2)

- [Fine-tuning MLLMs Without Forgetting Is Easier Than You Think](https://mlsanity.com/paper/2603.14493/) (2026)
- [Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning](https://mlsanity.com/paper/2605.14717/) (2026)

## Abstract (arXiv)

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.