A transformer-based computational approach for H&E to multiplexed immunohistochemistry stain translation

Chang Bian; Tim Cootes; Martin Fergie

doi:10.1117/12.2653590

6 April 2023 A transformer-based computational approach for H&E to multiplexed immunohistochemistry stain translation

Chang Bian, Tim Cootes, Martin Fergie

Proceedings Volume 12471, Medical Imaging 2023: Digital and Computational Pathology; 1247108 (2023) https://doi.org/10.1117/12.2653590
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

We established a translation dataset that contains pixel-wise registered H&E and multiplexed immunohistochemistry (mIHC) staining images. Deep learning models were trained to translate H&E inputs into their corresponding mIHC image versions. Comparison experiments have been carried out to validate the translation performance between TransUNet, U-Net, and pix2pix models. We also compared the impact of different Losses on model performances. The TransUNet model could achieve 0.862 SSIM score for L1 loss and 0.805 for L2 loss, surpassing U-Net and pix2pix model in both settings. This demonstrates the potential benefit of the Transformer module in stain translation tasks.

Conference Presentation

Citation Download Citation

Chang Bian, Tim Cootes, and Martin Fergie "A transformer-based computational approach for H&E to multiplexed immunohistochemistry stain translation", Proc. SPIE 12471, Medical Imaging 2023: Digital and Computational Pathology, 1247108 (6 April 2023); https://doi.org/10.1117/12.2653590

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