Title: CHAMMI-75: pre-training multi-channel models with heterogeneous microscopy images
Primary area: datasets and benchmarks
Keywords: microscopy, representation learning, multi-channel imaging, self-supervised learning, biology

Abstract: Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. However, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), or because the target experimental conditions are out of distribution. Here, we present CHAMMI-75, a dataset of heterogeneous, multi-channel microscopy images with more than 1.8B single cells from 75 diverse biological studies. We curated this resource from publicly available sources to investigate cellular morphology models that are channel-adaptive and can process any microscopy image type. Our experiments show that training with CHAMMI-75 can improve performance in multi-channel bioimaging tasks, opening the way to create the next generation of cellular morphology models for biological studies.

TL;DR: Dataset for pre-training multi-channel imaging models in microscopy and benchmarks in cellular biology applications