The widespread use of histopathology images, particularly those stained with hematoxylin and eosin (H&E), has proven pivotal for detecting tumors. However, advancing cancer cases to appropriate treatment pathways requires accurate quantification of human epidermal growth factor receptor 2 (HER2) protein expression. Predicting both low- and high-level expressions of HER2 remains challenging. Moreover, the joint analysis of H&E and immunohistochemistry (IHC) stained images for HER2 scoring is difficult. Although several deep learning methods have been investigated to address this clinical task, they fail to provide pixel-level localization of HER2 status. In this study, we propose a single end-to-end pipeline using a system of vision transformers with HER2 status scoring on whole slide images of WSIs. The method includes patch-wise processing of H&E WSIs for tumor localization. A novel mapping function is proposed to correspondingly identify correlated IHC WSI regions with malignant regions on H&E. A clinically inspired HER2 scoring mechanism is embedded in the pipeline and allows for automatic pixel-level annotation of 4-way HER2 scoring (0, 1+, 2+, and 3+). Also, the proposed method accurately returns HER2-negative and HER2-positive. Privately curated datasets were collaboratively extracted from 13 different cases of WSIs of H&E and IHC. A thorough experiment was conducted on the proposed method. Results obtained showed good classification accuracy during tumor localization. Also, a classification accuracy of 0.94 and a specificity of 0.933 were obtained for the prediction of HER2 status, scoring in the 4-way methods. The applicability of the proposed pipeline was investigated using WSIs patches as comparable to human pathologists. Findings from the study showed the usability of jointly evaluated H&E and IHC images on end-to-end ViT-based models for HER2 scoring.
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