Feb. 12, 2024, 5:42 a.m. | Henry Pinkard Cherry Liu Fanice Nyatigo Daniel A. Fletcher Laura Waller

cs.LG updates on arXiv.org arxiv.org

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 …

algorithms berkeley computational cost cs.cv cs.lg dataset datasets hardware imaging information machine machine learning making microscopy performance q-bio.qm sample shows systems types

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