KEYWORDS: Data modeling, Education and training, Transformers, Performance modeling, Visualization, Deep learning, Visual process modeling, Multiplexing, Image processing, Diagnostics
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.