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

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CyCIF quantifies multiple biomarkers, but panel capacity is compromised by technical challenges including tissue loss. We propose a computational panel reduction, inferring surrogate CyCIF data from a subset of biomarkers. Our model reconstructs the information content from 25 markers using only 9 m...

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Detalles Bibliográficos
Autores principales: Sims, Zachary, Mills, Gordon B., Chang, Young Hwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461912/
https://www.ncbi.nlm.nih.gov/pubmed/37645765
http://dx.doi.org/10.1101/2023.05.10.540265
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author Sims, Zachary
Mills, Gordon B.
Chang, Young Hwan
author_facet Sims, Zachary
Mills, Gordon B.
Chang, Young Hwan
author_sort Sims, Zachary
collection PubMed
description CyCIF quantifies multiple biomarkers, but panel capacity is compromised by technical challenges including tissue loss. We propose a computational panel reduction, inferring surrogate CyCIF data from a subset of biomarkers. Our model reconstructs the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer tissue microarrays, illustrating broader applicability to diverse tissue types.
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spelling pubmed-104619122023-08-29 MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation Sims, Zachary Mills, Gordon B. Chang, Young Hwan bioRxiv Article CyCIF quantifies multiple biomarkers, but panel capacity is compromised by technical challenges including tissue loss. We propose a computational panel reduction, inferring surrogate CyCIF data from a subset of biomarkers. Our model reconstructs the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer tissue microarrays, illustrating broader applicability to diverse tissue types. Cold Spring Harbor Laboratory 2023-08-16 /pmc/articles/PMC10461912/ /pubmed/37645765 http://dx.doi.org/10.1101/2023.05.10.540265 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Sims, Zachary
Mills, Gordon B.
Chang, Young Hwan
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/PMC10461912/
https://www.ncbi.nlm.nih.gov/pubmed/37645765
http://dx.doi.org/10.1101/2023.05.10.540265
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AT changyounghwan mimcycifmaskedimagingmodelingforenhancingcyclicimmunofluorescencecycifwithpanelreductionandimputation

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