MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation

Research output: Contribution to journalArticlepeer-review

Abstract

Cyclic Immunofluorescence (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.

Original languageEnglish (US)
Article number409
JournalCommunications Biology
Volume7
Issue number1
DOIs
StatePublished - Dec 2024

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences

Fingerprint

Dive into the research topics of 'MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation'. Together they form a unique fingerprint.

Cite this