TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge
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Abstract (arXiv)
The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an estimate of the proportion of the stromal region that contains TILs to obtain a TILs score. The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model. For this challenge, as the TIAger team, we have developed an algorithm to first segment tumor vs. stroma, before localising the tumor bulk region for TILs detection. Finally, we use these outputs to generate a TILs score for each case. On preliminary testing, our approach achieved a tumor-stroma weighted Dice score of 0.791 and a FROC score of 0.572 for lymphocytic detection. For predicting survival, our model achieved a C-index of 0.719. These results achieved first place across the preliminary testing leaderboards of the TiGER challenge.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 8 of its 12 references that are in the map. First posted on arXiv 23 Jun 2022.
Builds on
8 of 12 references.
References · 8
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Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 2.
7 papers.
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- 2025G2L:From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Knowledge Distillation13 Oct 2025
- 2025A Unified Low-level Foundation Model for Enhancing Pathology Image Quality1 Sep 2025
- 2025Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation26 Apr 2025
- 2024Tissue Concepts: supervised foundation models in computational pathology5 Sep 2024
- 2023Domain Generalization in Computational Pathology: Survey and Guidelines30 Oct 2023