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Unlike color photography images, which are consistently encoded into RGBchannels, biological images encompass various modalities, where the type ofmicroscopy and the meaning of each channel varies with each experiment.Importantly, the number of channels can range from one to a dozen and theircorrelation is often comparatively much lower than RGB, as each of them bringsspecific information content. This aspect is largely overlooked by methodsdesigned out of the bioimage field, and current solutions mostly focus onintra-channel spatial attention, often ignoring the relationship betweenchannels, yet crucial in most biological applications. Importantly, thevariable channel type and count prevent the projection of several experimentsto a unified representation for large scale pre-training. In this study, wepropose ChAda-ViT, a novel Channel Adaptive Vision Transformer architectureemploying an Inter-Channel Attention mechanism on images with an arbitrarynumber, order and type of channels. We also introduce IDRCell100k, a bioimagedataset with a rich set of 79 experiments covering 7 microscope modalities,with a multitude of channel types, and channel counts varying from 1 to 10 perexperiment. Our proposed architecture, trained in a self-supervised manner,outperforms existing approaches in several biologically relevant downstreamtasks. Additionally, it can be used to bridge the gap for the first timebetween assays with different microscopes, channel numbers or types byembedding various image and experimental modalities into a unified biologicalimage representation. The latter should facilitate interdisciplinary studiesand pave the way for better adoption of deep learning in biological image-basedanalyses. Code and Data to be released soon.

ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images