Improving stain normalization for digital histological image analysis based on the cycle generative adversarial network identity loss model

Objective Stain color variations caused by differences in staining environments and scanning devices pose a major challenge for deep learning–based analysis of digital histopathological images. This study aims to develop a robust stain normalization framework that preserves structural information while enabling stable color-domain conversion across heterogeneous stain domains. Methods We propose a generative adversarial network (GAN)–based training and testing framework, termed I-GAN, which integrates StainGAN and Stain-to-Stain Translation (STST). The method incorporates identity loss within an RGB–grayscale training strategy and applies RGB images during testing to preserve original stain information. Performance was evaluated on the MITOS-ATYPIA 14 dataset using SSIM, PSNR, and DeltaE-ITP, and further assessed on downstream classification tasks using Camelyon17 and the ICIAR2018 BACH Challenge datasets. Results On MITOS-ATYPIA 14, I-GAN achieved an SSIM of 0.980, a PSNR of 29.579, and a DeltaE-ITP of 46.284, indicating superior structural preservation and color fidelity. For classification tasks, I-GAN obtained an average precision of 0.964 on Camelyon17 and an accuracy of 0.87, precision of 0.86, and recall of 0.87 on the ICIAR2018 BACH dataset. Conclusions The proposed I-GAN framework improves stain normalization for hematoxylin and eosin–stained digital histopathology images by preserving structural integrity and achieving accurate color-domain conversion. These results demonstrate the robustness and practical applicability of the proposed approach for medical image analysis.