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6 April 2023A transformer-based computational approach for H&E to multiplexed immunohistochemistry stain translation
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Chang Bian,1 Tim Cootes,1 Martin Fergie1

1The Univ. of Manchester (United Kingdom)
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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.
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Chang Bian,Tim Cootes, andMartin 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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KEYWORDS
Data modeling
Education and training
Transformers
Performance modeling
Deep learning
Visualization
Diagnostics
Image processing
Multiplexing
Visual process modeling
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Chang Bian, Tim Cootes, 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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