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In the biomedical domain, there is an abundance of dense, complex data whereobjects of interest may be challenging to detect or constrained by limits ofhuman knowledge. Labelled domain specific datasets for supervised tasks areoften expensive to obtain, and furthermore discovery of novel distinct objectsmay be desirable for unbiased scientific discovery. Therefore, we proposeleveraging the wealth of annotations in benchmark computer vision datasets toconduct unsupervised instance segmentation for diverse biomedical datasets. Thekey obstacle is thus overcoming the large domain shift from common tobiomedical images. We propose a Domain Adaptive Region-based ConvolutionalNeural Network (DARCNN), that adapts knowledge of object definition from COCO,a large labelled vision dataset, to multiple biomedical datasets. We introducea domain separation module, a self-supervised representation consistency loss,and an augmented pseudo-labelling stage within DARCNN to effectively performdomain adaptation across such large domain shifts. We showcase DARCNN'sperformance for unsupervised instance segmentation on numerous biomedicaldatasets.

DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images