A scalable data management framework for image flow cytometry research using OMERO
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| Názov: | A scalable data management framework for image flow cytometry research using OMERO |
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| Autori: | Massei, Riccardo |
| Informácie o vydavateľovi: | Zenodo, 2025. |
| Rok vydania: | 2025 |
| Predmety: | OMERO, Galaxy, RDM, Flow Cytometry |
| Popis: | Recent advances in flow cytometry have incorporated imaging capabilities, enabling the high-throughput analysis of cellular morphology and subcellular structures.The integration of machine learning and artificial intelligence algorithms has further enhanced data interpretation, facilitating automated classification and feature extraction. The large-scale image and metadata information generated by this approach present significant challenges, including substantial storage requirements, standardization issues, and computational demands for processing high-dimensional information. |
| Druh dokumentu: | Conference object |
| Jazyk: | English |
| DOI: | 10.5281/zenodo.17222909 |
| DOI: | 10.5281/zenodo.17222908 |
| Rights: | CC BY |
| Prístupové číslo: | edsair.doi.dedup.....2a13ddc364f70c70cfbe15345946f79e |
| Databáza: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | Recent advances in flow cytometry have incorporated imaging capabilities, enabling the high-throughput analysis of cellular morphology and subcellular structures.The integration of machine learning and artificial intelligence algorithms has further enhanced data interpretation, facilitating automated classification and feature extraction. The large-scale image and metadata information generated by this approach present significant challenges, including substantial storage requirements, standardization issues, and computational demands for processing high-dimensional information. |
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| DOI: | 10.5281/zenodo.17222909 |