  Staining is critical to cell imaging and medical diagnosis, which is
expensive, time-consuming, labor-intensive, and causes irreversible changes to
cell tissues. Recent advances in deep learning enabled digital staining via
supervised model training. However, it is difficult to obtain large-scale
stained/unstained cell image pairs in practice, which need to be perfectly
aligned with the supervision. In this work, we propose a novel unsupervised
deep learning framework for the digital staining of cell images using knowledge
distillation and generative adversarial networks (GANs). A teacher model is
first trained mainly for the colorization of bright-field images. After that,a
student GAN for staining is obtained by knowledge distillation with hybrid
non-reference losses. We show that the proposed unsupervised deep staining
method can generate stained images with more accurate positions and shapes of
the cell targets. Compared with other unsupervised deep generative models for
staining, our method achieves much more promising results both qualitatively
and quantitatively.
