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.