Beyond attention: deriving biologically interpretable insights from
  weakly-supervised multiple-instance learning models
Willem Bonnaffé CRUK ICGC Prostate Group Freddie Hamdy Yang Hu Ian Mills Jens Rittscher Clare Verrill Dan J. Woodcock
Recent advances in attention-based multiple instance learning (MIL) have
improved our insights into the tissue regions that models rely on to make
predictions in digital pathology. However, the interpretability of these
approaches is still limited. In particular, they do not report whether
high-attention regions are positively or negatively associated with the class
labels or how well these regions correspond to previously established clinical
and biological knowledge. We address this by introducing a post-training
methodology to analyse MIL models. Firstly, we introduce
prediction-attention-weighted (PAW) maps by combining tile-level attention and
prediction scores produced by a refined encoder, allowing us to quantify the
predictive contribution of high-attention regions. Secondly, we introduce a
biological feature instantiation technique by integrating PAW maps with nuclei
segmentation masks. This further improves interpretability by providing
biologically meaningful features related to the cellular organisation of the
tissue and facilitates comparisons with known clinical features. We illustrate
the utility of our approach by comparing PAW maps obtained for prostate cancer
diagnosis (i.e. samples containing malignant tissue, 381/516 tissue samples)
and prognosis (i.e. samples from patients with biochemical recurrence following
surgery, 98/663 tissue samples) in a cohort of patients from the international
cancer genome consortium (ICGC UK Prostate Group). Our approach reveals that
regions that are predictive of adverse prognosis do not tend to co-locate with
the tumour regions, indicating that non-cancer cells should also be studied
when evaluating prognosis.