Human epidermal growth factor receptor 2 (HER2) expression is a critical biomarker for assessing breast cancer (BC) severity and guiding targeted anti-HER2 therapies. The standard method for measuring HER2 expression is manual assessment of IHC slides by pathologists, which is both time intensive and prone to inter- and intra-observer variability. To address these challenges, we developed an interpretable deep-learning pipeline with Correlational Attention Neural Network (Corr-A-Net) to predict HER2 score from H&E images. Each prediction was accompanied with a confidence score generated by the surrogate confidence score estimation network trained using incentivized mechanism. The shared correlated representations generated using the attention mechanism of Corr-A-Net achieved the best predictive accuracy of 0.93 and AUC-ROC of 0.98. Additionally, correlated representations demonstrated the highest mean effective confidence (MEC) score of 0.85 indicating robust confidence level estimation for prediction. The Corr-A-Net can have profound implications in facilitating prediction of HER2 status from H&E images.

The authors have declared no competing interest.

This work was supported by NIH/NCI grants U24CA209837, U24CA253531 and internal funds provided by the Siteman Cancer Center (SCC) and Mallinckrodt Institute of Radiology (MIR). The Center of High-Performance Computing (CHPC) at Washington University in St. Louis for computing resources supported by NIH grant number S10OD025200.

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The public dataset used in this study can be downloaded from https://bupt-ai-cz.github.io/BCI/

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All data produced in the present study are available upon reasonable request to the authors