HEMIT：基于双分支Pix2pix生成器的H&E到多重免疫组织化学图像转换
HEMIT: H&E to Multiplex-immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator
AI总结：
提出细胞级对齐数据集HEMIT用于H&E到多重免疫组织化学图像转换，并设计双分支生成器架构结合残差CNN和Swin Transformers，在多项指标上优于现有算法，为染色转换任务设定新基准。
摘要：
多重免疫荧光组织学数据的计算分析正成为理解癌症肿瘤微环境的重要方法。本工作提出了HEMIT，这是一个设计用于将苏木精和伊红（H&E）切片转换为多重免疫组织化学（mIHC）图像的数据集，包含DAPI、CD3和panCK标记物。独特的是，HEMIT的mIHC图像是多组分的，并且在细胞水平上与H&E对齐，丰富了有监督的染色转换任务。据我们所知，HEMIT是首个公开可用的细胞级对齐数据集，能够实现H&E到多靶点mIHC图像转换。该数据集为计算机视觉社区提供了宝贵资源，以开发新型计算方法，有望从H&E切片档案中获取新见解。我们还提出了一种新的双分支生成器架构，使用残差卷积神经网络（CNNs）和Swin Transformers，取得了比其他流行算法更好的转换效果。在HEMIT上评估时，它优于pix2pixHD、pix2pix、U-Net和ResNet，在结构相似性指数测度（SSIM）、皮尔逊相关分数（R）和峰值信噪比（PSNR）等关键指标上取得最高总分。此外，下游分析已被用于进一步验证生成的mIHC图像质量。这些结果在染色转换任务领域设定了新基准。
英文摘要：
Computational analysis of multiplexed immunofluorescence histology data is emerging as an important method for understanding the tumour micro-environment in cancer. This work presents HEMIT, a dataset designed for translating Hematoxylin and Eosin (H&E) sections to multiplex-immunohistochemistry (mIHC) images, featuring DAPI, CD3, and panCK markers. Distinctively, HEMIT's mIHC images are multi-component and cellular-level aligned with H&E, enriching supervised stain translation tasks. To our knowledge, HEMIT is the first publicly available cellular-level aligned dataset that enables H&E to multi-target mIHC image translation. This dataset provides the computer vision community with a valuable resource to develop novel computational methods which have the potential to gain new insights from H&E slide archives. We also propose a new dual-branch generator architecture, using residual Convolutional Neural Networks (CNNs) and Swin Transformers which achieves better translation outcomes than other popular algorithms. When evaluated on HEMIT, it outperforms pix2pixHD, pix2pix, U-Net, and ResNet, achieving the highest overall score on key metrics including the Structural Similarity Index Measure (SSIM), Pearson correlation score (R), and Peak signal-to-noise Ratio (PSNR). Additionally, downstream analysis has been used to further validate the quality of the generated mIHC images. These results set a new benchmark in the field of stain translation tasks.
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