Ultra-high-resolution unpaired stain transformation via Kernelized Instance Normalization

M. Ho, Min Wu, Che-Ming Wu
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引用次数: 3

Abstract

While hematoxylin and eosin (H&E) is a standard staining procedure, immunohistochemistry (IHC) staining further serves as a diagnostic and prognostic method. However, acquiring special staining results requires substantial costs. Hence, we proposed a strategy for ultra-high-resolution unpaired image-to-image translation: Kernelized Instance Normalization (KIN), which preserves local information and successfully achieves seamless stain transformation with constant GPU memory usage. Given a patch, corresponding position, and a kernel, KIN computes local statistics using convolution operation. In addition, KIN can be easily plugged into most currently developed frameworks without re-training. We demonstrate that KIN achieves state-of-the-art stain transformation by replacing instance normalization (IN) layers with KIN layers in three popular frameworks and testing on two histopathological datasets. Furthermore, we manifest the generalizability of KIN with high-resolution natural images. Finally, human evaluation and several objective metrics are used to compare the performance of different approaches. Overall, this is the first successful study for the ultra-high-resolution unpaired image-to-image translation with constant space complexity. Code is available at: https://github.com/Kaminyou/URUST
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基于核实例归一化的超高分辨率非配对染色变换
虽然苏木精和伊红(H&E)是标准的染色方法,但免疫组织化学(IHC)染色还可以作为诊断和预后的方法。然而,获得特殊的染色效果需要大量的成本。因此,我们提出了一种超高分辨率非配对图像到图像转换的策略:kernel - ized Instance Normalization (KIN),该策略保留了局部信息,并成功地在恒定的GPU内存使用下实现了无缝的染色转换。给定一个补丁、对应的位置和一个内核,KIN使用卷积运算计算局部统计信息。此外,KIN可以很容易地插入到大多数当前开发的框架中,而无需重新培训。我们证明,通过在三个流行的框架中用KIN层替换实例规范化(IN)层,并在两个组织病理学数据集上进行测试,KIN实现了最先进的染色转换。此外,我们用高分辨率自然图像证明了KIN的泛化性。最后,利用人的评价和几个客观指标来比较不同方法的性能。总的来说,这是首次成功研究具有恒定空间复杂度的超高分辨率非配对图像到图像的翻译。代码可从https://github.com/Kaminyou/URUST获得
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