将H&E图像转化为IHC：一种变异性惩罚的GAN用于精准肿瘤学
Transforming H&E images into IHC: A Variance-Penalized GAN for Precision Oncology
AI总结：
本研究提出一种变异性惩罚的GAN模型，用于将H&E图像转化为高保真的IHC图像，以提升HER2阳性乳腺癌的诊断效率和精度。
摘要：
人表皮生长因子受体2（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）是评估HER2的标准技术，但成本高、劳动强度大且高度依赖于抗体选择。相比之下，苏木伊红（H&E）染色是一种常规的组织病理学程序，提供了更广泛可及性，但缺乏HER2特异性。本研究提出了一种先进的深度学习图像转换框架，旨在从H&E染色的组织样本中生成高保真的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）中的模式崩溃问题，并引入了一种新的基于方差的惩罚项，以强制生成图像的结构多样性。我们的模型特别擅长于转换HER2阳性（IHC 3+）图像，这些图像一直是现有方法难以处理的。在整体BCI数据集上的定量评估表明，我们的方法优于基线模型，达到了峰值信噪比（PSNR）为22.16，结构相似性指数（SSIM）为0.47，以及弗雷切陷入门距离（FID）为346.37。相比之下，金字塔pix2pix基线达到了PSNR 21.15，SSIM 0.43，FID 516.75，而标准pix2pix模型达到了PSNR 20.74，SSIM 0.44，FID 472.6。这些结果证实了我们生成的IHC图像在保真度和现实感方面的优越性。除了医学成像外，我们的模型在一般图像到图像转换任务中也表现出色，展示了其在多个领域中的潜力。这项工作标志着向AI驱动的精准肿瘤学迈出的重要一步，提供了一种可靠且高效的替代传统HER2诊断方法。
英文摘要：
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 high-fidelity 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. Quantitative evaluations on the overall BCI dataset reveal that our approach outperforms baseline models, achieving a peak signal-to-noise ratio (PSNR) of 22.16, a structural similarity index (SSIM) of 0.47, and a Fréchet Inception Distance (FID) of 346.37. In comparison, the pyramid pix2pix baseline attained PSNR 21.15, SSIM 0.43, and FID 516.75, while the standard pix2pix model yielded PSNR 20.74, SSIM 0.44, and FID 472.6. These results affirm the superior fidelity and realism of our generated IHC 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.
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