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Stain Style Transfer Using Transitive Adversarial Networks

  • Shaojin Cai
  • Yuyang Xue
  • Qinquan Gao
  • Min Du
  • Gang Chen
  • Hejun Zhang
  • Tong TongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)

Abstract

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 0.87 dB in terms of PSNR, demonstrating the effectiveness of the proposed TAN method in stain style transfer.

Keywords

Pathological slides Stain transfer Color transfer Generative adversarial networks 

Notes

Acknowledgement

This study was supported by the National Natural Science Foundation of China (Grant No. 6190010435) and the Science and Technology Program of Fujian Province, China (Grant No. 2019YZ016006).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shaojin Cai
    • 1
    • 2
  • Yuyang Xue
    • 3
  • Qinquan Gao
    • 1
    • 2
    • 3
  • Min Du
    • 1
    • 2
  • Gang Chen
    • 4
  • Hejun Zhang
    • 4
  • Tong Tong
    • 1
    • 2
    • 3
    Email author
  1. 1.College of Physics and Information EngineeringFuzhou UniversityFuzhouChina
  2. 2.Fujian Key Lab of Medical Instrumentation and Pharmaceutical TechnologyFuzhouChina
  3. 3.Imperial Vision TechnologyFuzhouChina
  4. 4.Department of Pathology, Fujian Provincial Cancer HospitalThe Affiliated Hospital of Fujian Medical UniversityFuzhouChina

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