  Most neural networks assume that input images have a fixed number of channels
(three for RGB images). However, there are many settings where the number of
channels may vary, such as microscopy images where the number of channels
changes depending on instruments and experimental goals. Yet, there has not
been a systemic attempt to create and evaluate neural networks that are
invariant to the number and type of channels. As a result, trained models
remain specific to individual studies and are hardly reusable for other
microscopy settings. In this paper, we present a benchmark for investigating
channel-adaptive models in microscopy imaging, which consists of 1) a dataset
of varied-channel single-cell images, and 2) a biologically relevant evaluation
framework. In addition, we adapted several existing techniques to create
channel-adaptive models and compared their performance on this benchmark to
fixed-channel, baseline models. We find that channel-adaptive models can
generalize better to out-of-domain tasks and can be computationally efficient.
We contribute a curated dataset (https://doi.org/10.5281/zenodo.7988357) and an
evaluation API (https://github.com/broadinstitute/MorphEm.git) to facilitate
objective comparisons in future research and applications.
