Summary of The Berkeley Single Cell Computational Microscopy (bsccm) Dataset, by Henry Pinkard et al.
The Berkeley Single Cell Computational Microscopy (BSCCM) Dataset
by Henry Pinkard, Cherry Liu, Fanice Nyatigo, Daniel A. Fletcher, Laura Waller
First submitted to arxiv on: 9 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the Berkeley Single Cell Computational Microscopy (BSCCM) dataset, which is designed to standardize performance evaluation for machine learning-based imaging systems. The dataset comprises over 12 million images of individual white blood cells, each captured using multiple illumination patterns on an LED array microscope and accompanied by fluorescent measurements of surface proteins. This rich resource can facilitate the development and testing of new algorithms in computational microscopy and computer vision, with potential applications in biomedical research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a big dataset to help scientists compare different ways to make images from tiny things like cells. They want to make sure that any new ideas they come up with work well on lots of different samples. The dataset has millions of pictures taken with special lighting and sensors, which can tell us what kind of cell it is just by looking at its surface. This will help scientists improve their imaging systems and do more cool things in biology and medicine. |
Keywords
* Artificial intelligence * Machine learning
