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.

Hematoxylin and eosin (H&E) stained colors is a critical step in the digitized pathological diagnosis of cancer. However, differences in section preparations, staining protocols and scanner specifications may result in the variations of stain colors in pathological images, which can potentially hamper the effectiveness of pathologist's diagnosis and the robustness. To alleviate this problem, several color normalization methods have been proposed. Most previous approaches map color information between images highly dependent on a reference template. However, due to the problem that pathological images are usually unpaired, these methods cannot produce satisfactory results. In this work, we propose an unsupervised color normalization method based on channel attention and long-range residual, using a technology called invertible neural networks (INN) to transfer the stain style while preserving the tissue semantics between different hospitals or centers, resulting in a virtual stained sample in the sense that no actual stains are used. In our method, the expert does not need to choose a template image. More specifically, we have developed a new unsupervised stain style transfer framework based on INN that is different from state-of-the-art methods. Meanwhile, we propose a new generator and a discriminator to further improve the performance. Our approach outperforms state-of-the-art methods both in objective metrics and subjective evaluations, yielding an improvement of 1.0 dB in terms of PSNR. Moreover, the amount of computation of the proposed network has been reduced by 33 %. This indicates that the inference speed is almost one third faster while the performance is better. INDEX TERMSColor normalization, stain style transfer, invertible neural networks, pathological images.

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