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

• URL: http://arxiv.org/abs/2506.18371v1
• Date: Mon, 23 Jun 2025 07:57:22 GMT
• Title: Transforming H&E images into IHC: A Variance-Penalized GAN for Precision Oncology
• Authors: Sara Rehmat, Hafeez Ur Rehman,
• Abstract summary: This study proposes an advanced deep learning-based image translation framework to generate highfidelity IHC images from H&E-stained tissue samples.<n>By modifying the loss function of pyramid pix2pix, we mitigate mode collapse, a fundamental limitation in generative adversarial networks (GANs)<n>Our model particularly excels in translating HER2-positive (IHC 3+) images, which have remained challenging for existing methods due to their complex morphological variations.
• Score: 1.0972875392165036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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