Fig 1. Training of discriminator network.

For real examples, we used the real images and their segmentation/annotation masks (Mⁱ, $I_r^i$) as an input. The green and red colored annotations correspond to Ki67 positive and Ki67 negative nuclei, respectively. For fake examples, we applied a two-step procedure. In Step 1, we used generator (U-net) algorithm to create a synthetic image by using the segmentation/annotation. In Step 2, the output of the generator and initial segmentation (Mⁱ, $I_g^i$) are used as an input for D.