Foundation models, a growing class of deep learning models trained for broad utility, are revolutionizing the analysis of high-dimensional microscopy image data. In the context of drug discovery, these models can be trained to learn rich, biologically informative embeddings from diverse image types. In this presentation, we highlight a channel-agnostic foundation model, Phenom-Beta, which excels in high-performance biologically relevant benchmark tasks. We demonstrate how these embeddings support applications in cellular phenotyping, high-content screening, and multi-modal data integration, offering enhanced capabilities in detecting genetic and chemical perturbations and improving the accuracy of biological research.
Learning Objectives:
Describe the fundamentals of foundation model development, including dataset curation.
Understand the value-proposition of foundation models for microscopy image analysis.
Find resources for ML-based microscopy image analysis, including pretrained image foundation models.
