An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 10 Nov 2023.
Builds on
12 of 21 references.
References · 12
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Citing papers per year: 2026: 1.
1 paper.