MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation

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CyCIF can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increa...

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Detalles Bibliográficos
Autores principales: Chang, Young Hwan, Sims, Zachary, Mills, Gordon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543389/
https://www.ncbi.nlm.nih.gov/pubmed/37790506
http://dx.doi.org/10.21203/rs.3.rs-3270272/v1
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author Chang, Young Hwan
Sims, Zachary
Mills, Gordon
author_facet Chang, Young Hwan
Sims, Zachary
Mills, Gordon
author_sort Chang, Young Hwan
collection PubMed
description CyCIF can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.
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spelling pubmed-105433892023-10-03 MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation Chang, Young Hwan Sims, Zachary Mills, Gordon Res Sq Article CyCIF can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types. American Journal Experts 2023-09-21 /pmc/articles/PMC10543389/ /pubmed/37790506 http://dx.doi.org/10.21203/rs.3.rs-3270272/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Chang, Young Hwan
Sims, Zachary
Mills, Gordon
MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation
title MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation
title_full MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation
title_fullStr MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation
title_full_unstemmed MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation
title_short MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation
title_sort mim-cycif: masked imaging modeling for enhancing cyclic immunofluorescence (cycif) with panel reduction and imputation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543389/
https://www.ncbi.nlm.nih.gov/pubmed/37790506
http://dx.doi.org/10.21203/rs.3.rs-3270272/v1
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AT millsgordon mimcycifmaskedimagingmodelingforenhancingcyclicimmunofluorescencecycifwithpanelreductionandimputation

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