  We present WeakSTIL, an interpretable two-stage weak label deep learning
pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes
(sTIL%) in H&E-stained whole-slide images (WSIs) of breast cancer tissue. The
sTIL% score is a prognostic and predictive biomarker for many solid tumor
types. However, due to the high labeling efforts and high intra- and
interobserver variability within and between expert annotators, this biomarker
is currently not used in routine clinical decision making. WeakSTIL compresses
tiles of a WSI using a feature extractor pre-trained with self-supervised
learning on unlabeled histopathology data and learns to predict precise sTIL%
scores for each tile in the tumor bed by using a multiple instance learning
regressor that only requires a weak WSI-level label. By requiring only a weak
label, we overcome the large annotation efforts required to train currently
existing TIL detection methods. We show that WeakSTIL is at least as good as
other TIL detection methods when predicting the WSI-level sTIL% score, reaching
a coefficient of determination of $0.45\pm0.15$ when compared to scores
generated by an expert pathologist, and an AUC of $0.89\pm0.05$ when treating
it as the clinically interesting sTIL-high vs sTIL-low classification task.
Additionally, we show that the intermediate tile-level predictions of WeakSTIL
are highly interpretable, which suggests that WeakSTIL pays attention to latent
features related to the number of TILs and the tissue type. In the future,
WeakSTIL may be used to provide consistent and interpretable sTIL% predictions
to stratify breast cancer patients into targeted therapy arms.
