使用传递对抗网络进行染色风格迁移
Stain Style Transfer using Transitive Adversarial Networks
AI总结：
针对医院间病理切片不成对导致颜色匹配困难的问题，提出传递对抗网络（TAN）进行染色风格迁移，无需专家挑选参考切片，相比最先进方法PSNR提高了0.87dB。
摘要：
近年来，数字化病理诊断的需求不断增加。众所周知，颜色信息对于病理切片的自动和视觉分析至关重要。然而，由各种因素引起的颜色变化不仅会对病理学家的诊断产生负面影响，还会降低算法的鲁棒性。导致颜色差异的因素不仅存在于切片制作过程中，也存在于数字化过程中。已经提出了不同的策略来缓解颜色变化。此类技术大多依赖于收集颜色统计信息来执行图像间的颜色匹配，并且高度依赖于参考模板切片。由于医院之间的病理切片通常是不成对的，这些方法无法产生良好的匹配结果。在这项工作中，我们提出了一种新网络，我们称之为传递对抗网络（TAN），用于在不同医院或中心的切片之间传递颜色信息。在提出的TAN方法中，专家无需挑选具有代表性的参考切片。我们将所提出的方法与最先进的方法进行了定量和定性的比较。与最先进的方法相比，我们的方法在PSNR方面提高了0.87dB，证明了所提出的TAN方法在染色风格迁移中的有效性。
英文摘要：
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.
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