  Digitized pathological diagnosis has been in increasing demand recently. It
is well known that color information is critical to the automatic and visual
analysis of pathological slides. However, the color variations due to various
factors not only have negative impact on pathologist's diagnosis, but also will
reduce the robustness of the algorithms. The factors that cause the color
differences are not only in the process of making the slices, but also in the
process of digitization. Different strategies have been proposed to alleviate
the color variations. Most of such techniques rely on collecting color
statistics to perform color matching across images and highly dependent on a
reference template slide. Since the pathological slides between hospitals are
usually unpaired, these methods do not yield good matching results. In this
work, we propose a novel network that we refer to as Transitive Adversarial
Networks (TAN) to transfer the color information among slides from different
hospitals or centers. It is not necessary for an expert to pick a
representative reference slide in the proposed TAN method. We compare the
proposed method with the state-of-the-art methods quantitatively and
qualitatively. Compared with the state-of-the-art methods, our method yields an
improvement of 0.87dB in terms of PSNR, demonstrating the effectiveness of the
proposed TAN method in stain style transfer.
