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3 April 2024 An unsupervised workflow for explainable biomarker identification based on multiplex data
Tillmann Falck, Harald Hessel, Florian Song, Markus Schick, Corina Cotoi, Nicolas Brieu
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Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 1293316 (2024) https://doi.org/10.1117/12.2692908
Event: SPIE Medical Imaging, 2024, San Diego, California, United States
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Abstract
Discovery of explainable biomarkers is a complex process which is typically driven by a-priori hypothesis and expert annotations. This contribution introduces an almost entirely annotation and hypothesis free workflow to discover predictive biomarkers derived from cell phenotypes. It relies on self-supervised learning, clustering and survival analysis of cell centric image patches. The workflow is successfully evaluated on mIF images of 2L+ mNSCLC samples from a clinical study (NCT01693562). Two potential biomarkers are identified that closely align with the known relevant biology.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tillmann Falck, Harald Hessel, Florian Song, Markus Schick, Corina Cotoi, and Nicolas Brieu "An unsupervised workflow for explainable biomarker identification based on multiplex data", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 1293316 (3 April 2024); https://doi.org/10.1117/12.2692908
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KEYWORDS
Machine learning
Cell phenotyping
Multiplexing
Artificial intelligence
Deep learning
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