  Tumour-infiltrating lymphocytes (TILs) are considered as a valuable
prognostic markers in both triple-negative and human epidermal growth factor
receptor 2 (HER2) positive breast cancer. In this study, we introduce an
innovative deep learning pipeline based on the Efficient-UNet architecture to
predict the TILs score for breast cancer whole-slide images (WSIs). We first
segment tumour and stromal regions in order to compute a tumour bulk mask. We
then detect TILs within the tumour-associated stroma, generating a TILs score
by closely mirroring the pathologist's workflow. Our method exhibits
state-of-the-art performance in segmenting tumour/stroma areas and TILs
detection, as demonstrated by internal cross-validation on the TiGER Challenge
training dataset and evaluation on the final leaderboards. Additionally, our
TILs score proves competitive in predicting survival outcomes within the same
challenge, underscoring the clinical relevance and potential of our automated
TILs scoring pipeline as a breast cancer prognostic tool.
