CHAMMI: A benchmark for channel-adaptive models in microscopy imaging

We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers.