When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis
A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and d...
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| Vydáno v: | Nature methods Ročník 20; číslo 7; s. 968 - 970 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Nature Publishing Group US
01.07.2023
Nature Publishing Group |
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| ISSN: | 1548-7091, 1548-7105, 1548-7105 |
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| Abstract | A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and discuss possible solutions for automating and streamlining the validation process in the next five to ten years. |
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| AbstractList | A key step toward biologically interpretable analysis of microscopy image-based assays is rigorous quantitative validation with metrics appropriate for the particular application in use. Here we describe this challenge for both classical and modern deep learning-based image analysis approaches and discuss possible solutions for automating and streamlining the validation process in the next five to ten years. |
| Author | Chen, Jianxu Viana, Matheus P. Rafelski, Susanne M. |
| Author_xml | – sequence: 1 givenname: Jianxu orcidid: 0000-0002-8500-1357 surname: Chen fullname: Chen, Jianxu organization: Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V – sequence: 2 givenname: Matheus P. surname: Viana fullname: Viana, Matheus P. organization: Allen Institute for Cell Science – sequence: 3 givenname: Susanne M. orcidid: 0000-0002-1399-5970 surname: Rafelski fullname: Rafelski, Susanne M. email: susanner@alleninstitute.org organization: Allen Institute for Cell Science |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37433995$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1093_jmicro_dfad059 crossref_primary_10_1093_gigascience_giad120 crossref_primary_10_1038_s44303_025_00092_0 crossref_primary_10_1242_jcs_261567 crossref_primary_10_1371_journal_pcbi_1013058 crossref_primary_10_1016_j_ceb_2024_102378 crossref_primary_10_1016_j_jbc_2025_110593 crossref_primary_10_1002_advs_202307929 crossref_primary_10_1016_j_cels_2025_101265 crossref_primary_10_1186_s13040_024_00412_x |
| Cites_doi | 10.48550/arXiv.2302.07005 10.1016/j.cell.2018.03.040 10.1371/journal.pcbi.1008443 10.1038/s41592-018-0111-2 10.1038/s41592-022-01663-4 10.1038/s41586-022-05563-7 10.1016/j.xpro.2022.102040 10.20944/preprints202301.0552.v1 10.1038/s41592-018-0216-7 10.1038/s41598-020-77733-4 10.21203/rs.3.rs-1582100/v1 10.1109/TBME.2021.3117407 10.1145/3464423 10.1038/s41592-020-01008-z 10.1038/s41592-021-01080-z 10.1101/491035 10.48550/arXiv.2110.11334 10.48550/arXiv.2302.01790 |
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| Title | When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis |
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