  This paper presents a new regularization method to train a fully
convolutional network for semantic tissue segmentation in histopathological
images. This method relies on the benefit of unsupervised learning, in the form
of image reconstruction, for network training. To this end, it puts forward an
idea of defining a new embedding that allows uniting the main supervised task
of semantic segmentation and an auxiliary unsupervised task of image
reconstruction into a single one and proposes to learn this united task by a
single generative model. This embedding generates an output image by
superimposing an input image on its segmentation map. Then, the method learns
to translate the input image to this embedded output image using a conditional
generative adversarial network, which is known as quite effective for
image-to-image translations. This proposal is different than the existing
approach that uses image reconstruction for the same regularization purpose.
The existing approach considers segmentation and image reconstruction as two
separate tasks in a multi-task network, defines their losses independently, and
combines them in a joint loss function. However, the definition of such a
function requires externally determining right contributions of the supervised
and unsupervised losses that yield balanced learning between the segmentation
and image reconstruction tasks. The proposed approach provides an easier
solution to this problem by uniting these two tasks into a single one, which
intrinsically combines their losses. We test our approach on three datasets of
histopathological images. Our experiments demonstrate that it leads to better
segmentation results in these datasets, compared to its counterparts.
