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Human epidermal growth factor (HER2) is a predictive and prognostic biomarker whose degree of presence in breast cancer informs prognosis and therapeutic decision making. In clinical practice, it is routinely assessed using immunohistochemical (IHC) staining. A pathologist assigns a score from 0 to 3+ depending on the intensity and distribution of staining – 0 or 1+ scores are classified as HER2 negative, 3+ scores as HER2 positive, and 2+ as equivocal. Unfortunately, variations in HER2 staining and the subjectivity in scoring can lead to inaccuracies. Therefore, we sought to develop an automated method to predict HER2 scores from HER2 and H&E slide images. Our database consisted of 52 adjacent HER2 and H&E tissue sections. Positive regions on HER2 were segmented using a previously developed method. Using 13-fold cross-validation, a truncated Resnet18 was then trained to classify extracted patches using HER2 score as labels for positive regions and a score of 0 for negative regions. Using the same folds, attentionbased multiple instance learning was used to aggregate learned patch embeddings into overall slide-level embeddings, which were subsequently classified. The preliminary method achieved 88% accuracy on 0/1+ and 85% accuracy on 2+ and 3+. Subsequent preliminary experiments qualitatively demonstrate that identified positive regions from HER2 can successfully be transferred over to H&E via image registration. Furthermore, applying the proposed method to predict HER2 score from H&E demonstrates that attention is paid to HER2 positive regions on H&E. Results provide preliminary evidence that HER2 can be localized and therefore scored using H&E images alone.
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