  In the biomedical domain, there is an abundance of dense, complex data where
objects of interest may be challenging to detect or constrained by limits of
human knowledge. Labelled domain specific datasets for supervised tasks are
often expensive to obtain, and furthermore discovery of novel distinct objects
may be desirable for unbiased scientific discovery. Therefore, we propose
leveraging the wealth of annotations in benchmark computer vision datasets to
conduct unsupervised instance segmentation for diverse biomedical datasets. The
key obstacle is thus overcoming the large domain shift from common to
biomedical images. We propose a Domain Adaptive Region-based Convolutional
Neural Network (DARCNN), that adapts knowledge of object definition from COCO,
a large labelled vision dataset, to multiple biomedical datasets. We introduce
a domain separation module, a self-supervised representation consistency loss,
and an augmented pseudo-labelling stage within DARCNN to effectively perform
domain adaptation across such large domain shifts. We showcase DARCNN's
performance for unsupervised instance segmentation on numerous biomedical
datasets.
