Saad Afaf, Ghatwary Noha, Gasser Safa M, ElMahallawy Mohamed S
Electronics and Communications, Arab Academy for Science, Heliopolis, Cairo, 2033, Egypt.
Department of Electrical and Communications, The British University in Egypt, El Sherouk, Cairo, 11837, Egypt.
BMC Med Imaging. 2025 Jan 6;25(1):6. doi: 10.1186/s12880-024-01522-y.
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images. We propose a new IHC-GAN that enhances the Pix2PixHD model into a dual generator module, improving its performance and simplifying its structure. Furthermore, to strengthen feature extraction for HE-stained image classification, we integrate MobileNetV3 as the backbone network. The extracted features are then merged with those generated by the generator to improve overall performance. Moreover, the decoder's performance is enhanced by providing the related features from the classified labels by incorporating the adaptive instance normalization technique. The proposed IHC-GAN was trained and validated on a comprehensive dataset comprising 4,870 registered image pairs, encompassing a spectrum of HER2 expression levels. Our findings demonstrate promising results in translating H&E images to IHC-equivalent representations, offering a potential solution to reduce the costs associated with traditional HER2 assessment methods. We extensively validate our model and the current dataset. We compare it with state-of-the-art techniques, achieving high performance using different evaluation metrics, showing 0.0927 FID, 22.87 PSNR, and 0.3735 SSIM. The proposed approach exhibits significant enhancements over current GAN models, including an 88% reduction in Frechet Inception Distance (FID), a 4% enhancement in Learned Perceptual Image Patch Similarity (LPIPS), a 10% increase in Peak Signal-to-Noise Ratio (PSNR), and a 45% reduction in Mean Squared Error (MSE). This advancement holds significant potential for enhancing efficiency, reducing manpower requirements, and facilitating timely treatment decisions in breast cancer care.
浸润性乳腺癌的诊断和治疗规划需要准确评估人表皮生长因子受体2（HER2）的表达水平。虽然免疫组织化学技术（IHC）是HER2评估的金标准，但其实施可能需要大量资源且成本高昂。为了减少这些障碍并加快流程，我们提出了一种高效的深度学习模型，该模型可直接从苏木精和伊红（H&E）染色图像生成高质量的IHC染色图像。我们提出了一种新的IHC-GAN，将Pix2PixHD模型增强为双生成器模块，提高其性能并简化其结构。此外，为了加强对HE染色图像分类的特征提取，我们集成了MobileNetV3作为骨干网络。然后将提取的特征与生成器生成的特征合并，以提高整体性能。此外，通过结合自适应实例归一化技术，从分类标签中提供相关特征，增强了解码器的性能。所提出的IHC-GAN在一个包含4870对注册图像对的综合数据集上进行了训练和验证，涵盖了HER2表达水平的范围。我们的研究结果表明，在将H&E图像转换为等效IHC表示方面取得了有前景的结果，为降低与传统HER2评估方法相关的成本提供了潜在解决方案。我们对我们的模型和当前数据集进行了广泛验证。我们将其与最先进的技术进行比较，使用不同的评估指标实现了高性能，显示出0.0927的FID、22.87的PSNR和0.3735的SSIM。所提出的方法相对于当前的GAN模型有显著增强，包括Frechet Inception距离（FID）降低88%、学习感知图像块相似度（LPIPS）提高4%、峰值信噪比（PSNR）提高10%以及均方误差（MSE）降低45%。这一进展在提高乳腺癌护理效率、减少人力需求以及促进及时治疗决策方面具有巨大潜力。
