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.

翻译：大多数神经网络假设输入图像具有固定数量的通道（RGB图像为三个通道）。然而，在诸多场景中通道数量可能发生变化，例如显微镜图像中通道数量会因仪器和实验目标而异。目前尚无系统性研究来创建和评估对通道数量及类型保持不变的神经网络。因此，已训练模型仍局限于特定研究，难以复用于其他显微镜设置。本文提出了一个用于研究显微镜成像中通道自适应模型的基准测试，其包含：1）一个多通道单细胞图像数据集，2）一个具有生物学相关性的评估框架。此外，我们改进了若干现有技术以构建通道自适应模型，并在该基准测试上将其与固定通道基线模型进行性能对比。研究发现，通道自适应模型能更好地泛化至域外任务且具备计算效率优势。我们贡献了经整理的数据集（https://doi.org/10.5281/zenodo.7988357）和评估API（https://github.com/broadinstitute/MorphEm.git），以促进未来研究与应用中的客观比较。