  Biological imaging data are often partially confounded or contain unwanted
variability. Examples of such phenomena include variable lighting across
microscopy image captures, stain intensity variation in histological slides,
and batch effects for high throughput drug screening assays. Therefore, to
develop "fair" models which generalise well to unseen examples, it is crucial
to learn data representations that are insensitive to nuisance factors of
variation. In this paper, we present a strategy based on adversarial training,
capable of learning unsupervised representations invariant to confounders. As
an empirical validation of our method, we use deep convolutional autoencoders
to learn unbiased cellular representations from microscopy imaging.
