## 6 Conclusions  
In this work, a framework is proposed for the weakly supervised classification of HER2 overexpression status on H&E stained BCa WSI. The proposed approach integrates a CNN trained for HER2 scoring of individual H&E stained slide tiles, initialized with the network parameters pre-trained with data from IHC stained images. The objective of this initialization is to transfer some domain knowledge to the final training. The individual scores are aggregated on a single prediction per slide, returning the HER2 status label. Tested with the BRCA data subset, the proposed method attained suitable performance. These preliminary results indicate that it is possible to accurately infer BCa HER2 status solely from \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline \hline
**MLP Aggregation:** & & & & \\ \hline Proposed method & 53.3\% & 21.5\% & 81.2\% & 31.5\% \\ \hline w/out pretrained CNN weights & 50.0\% & 60.3\% & 51.8\% & 72\% \\ \hline
**Median Aggregation:** & & & & \\ \hline w/pretrained CNN weights & 50.0\% & 12.3\% & 7.8\% & 28.0\% \\ \hline w/out pretrained CNN weights & 52.2\% & 63.5\% & 66.5\% & 72.0\% \\ \hline
**Mean Aggregation:** & & & & \\ \hline w/pretrained CNN weights & 50.0\% & 12.3\% & 7.8\% & 28.0\% \\ \hline w/out pretrained CNN weights & 52.2\% & 63.5\% & 66.5\% & 72.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results on the BRCA test set. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline \hline
**MLP Aggregation:** & & & & \\ \hline proposed method & 83.3\% & 86.7\% & 89.6\% & 87.5\% \\ \hline w/out pretrained CNN weights & 62.5\% & 48.1\% & 39.1\% & 62.5\% \\ \hline
**Median Aggregation:** & & & & \\ \hline w/pretrained CNN weights & 50.0\% & 43.3\% & 78.6\% & 50.0\% \\ \hline w/out pretrained CNN weights & 62.5\% & 48.1\% & 39.1\% & 62.5\% \\ \hline
**Mean Aggregation:** & & & & \\ \hline w/pretrained CNN weights & 50.0\% & 43.3\% & 78.6\% & 50.0\% \\ \hline w/out pretrained CNN weights & 62.5\% & 48.1\% & 39.1\% & 62.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on the HER2 SC test set. H&E stained slides. The results of an ablation study suggest that the proposed method with MLP tile score aggregation is more promising than simpler aggregation methods (mean or median). Despite these results, further efforts should be devoted to performance improvements in this task. Firstly, the training of the tile HER2 scoring CNN and the aggregation MLP could be integrated into a single optimization process to achieve better performance. On the other hand, the aggregation of individual scores could use tile locations to take spatial consistency into account. Finally, the knowledge embedded in the networks through the pre-trained parameters could be better seized if input H&E tiles could be previously converted into IHC (possibly using generative adversarial models). Conceptualisation, S.P.O., J.R.P., T.G., J.S.C. and H.P.O. ; Methodology, S.P.O., J.R.P., T.G. and J.S.C. ; Data Curation, S.P.O. and R.C.-M.; Writing, S.P.O., J.R.P., T.G. and R.C.-M.; Review, J.S.C. and H.P.O. ; Clinical Supervision, R.C.-M. and M.-J.C.; Technical Supervision, J.S.C. and H.P.O. All authors have read and agreed to the published version of the manuscript. This work was partially funded by the Project \"CLARE: Computer-Aided Cervical Cancer Screening\" (POCI-01-0145-FEDER-028857), financially supported by FEDER through Operational Competitiveness Program-COMPETE 2020 and by National Funds through the Portuguese funding agency, FCT-Fundacao para a Ciencia e a Tecnologia, and also the FCT PhD grants \"SFRH/BD/139108/2018\" and \"SFRH/BD/137720/2018\".