标题： 将H&E图像转换为IHC：一种方差惩罚的GAN用于精准肿瘤学 显示英文标题

标题： Transforming H&E images into IHC: A Variance-Penalized GAN for Precision Oncology

摘要： HER2在乳腺细胞中的过表达是HER2阳性乳腺癌的关键驱动因素，这是一种高度侵袭性的亚型，需要精确的诊断和靶向治疗。免疫组化（IHC）是HER2评估的标准技术，但成本高、劳动强度大，并且高度依赖抗体选择。相比之下，苏木精和伊红（H&E）染色是一种常规的组织病理学程序，具有更广泛的可及性，但缺乏HER2特异性。本研究提出了一种先进的基于深度学习的图像翻译框架，可以从H&E染色的组织样本生成高保真IHC图像，从而实现成本效益高且可扩展的HER2评估。通过修改金字塔pix2pix的损失函数，我们缓解了生成对抗网络（GANs）中的模式崩溃这一基本限制，并引入了一种基于方差的惩罚项，以在生成的图像中强制结构多样性。我们的模型在翻译HER2阳性（IHC 3+）图像方面特别出色，由于其复杂的形态学变化，现有方法在这些图像上一直存在挑战。在BCI组织病理学数据集上的广泛评估表明，我们的模型在峰值信噪比（PSNR）、结构相似性指数（SSIM）和弗雷歇初始距离（FID）方面优于最先进的方法，特别是在准确翻译HER2阳性（IHC 3+）图像方面。除了医学影像外，我们的模型在一般的图像到图像翻译任务中也表现出色，展示了其在多个领域的潜力。这项工作标志着向AI驱动的精准肿瘤学迈出的重要一步，为传统的HER2诊断提供了一种可靠且高效的选择。 显示英文摘要

摘要： The overexpression of the human epidermal growth factor receptor 2 (HER2) in breast cells is a key driver of HER2-positive breast cancer, a highly aggressive subtype requiring precise diagnosis and targeted therapy. Immunohistochemistry (IHC) is the standard technique for HER2 assessment but is costly, labor-intensive, and highly dependent on antibody selection. In contrast, hematoxylin and eosin (H&E) staining, a routine histopathological procedure, offers broader accessibility but lacks HER2 specificity. This study proposes an advanced deep learning-based image translation framework to generate highfidelity IHC images from H&E-stained tissue samples, enabling cost-effective and scalable HER2 assessment. By modifying the loss function of pyramid pix2pix, we mitigate mode collapse, a fundamental limitation in generative adversarial networks (GANs), and introduce a novel variance-based penalty that enforces structural diversity in generated images. Our model particularly excels in translating HER2-positive (IHC 3+) images, which have remained challenging for existing methods due to their complex morphological variations. Extensive evaluations on the BCI histopathological dataset demonstrate that our model surpasses state-of-the-art methods in terms of peak signal-tonoise ratio (PSNR), structural similarity index (SSIM), and Frechet Inception Distance (FID), particularly in accurately translating HER2-positive (IHC 3+) images. Beyond medical imaging, our model exhibits superior performance in general image-to-image translation tasks, showcasing its potential across multiple domains. This work marks a significant step toward AI-driven precision oncology, offering a reliable and efficient alternative to traditional HER2 diagnostics.