CHAMMI-75: pre-training multi-channel models with heterogeneous microscopy images
This work addresses the need for channel-adaptive cellular morphology models in biological research, though it is incremental as it focuses on dataset creation and validation.
The authors tackled the problem of specialized cell morphology models that cannot be reused across biological studies due to mismatched imaging types, by creating CHAMMI-75, an open dataset of heterogeneous multi-channel microscopy images from 75 studies, which improves performance in multi-channel bioimaging tasks due to its high diversity.
Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellular morphology are typically trained with a single microscopy imaging type. 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, an open access dataset of heterogeneous, multi-channel microscopy images 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 primarily because of its high diversity in microscopy modalities. This work paves the way to create the next generation of cellular morphology models for biological studies.