Can uncertainty boost the reliability of AI-based diagnostic methods in digital pathology?. (arXiv:2112.09693v1 [cs.LG])

Deep learning (DL) has shown great potential in digital pathology
applications. The robustness of a diagnostic DL-based solution is essential for
safe clinical deployment. In this work we evaluate if adding uncertainty
estimates for DL predictions in digital pathology could result in increased
value for the clinical applications, by boosting the general predictive
performance or by detecting mispredictions. We compare the effectiveness of
model-integrated methods (MC dropout and Deep ensembles) with a model-agnostic
approach (Test time augmentation, TTA). Moreover, four uncertainty metrics are
compared. Our experiments focus on two domain shift scenarios: a shift to a
different medical center and to an underrepresented subtype of cancer. Our
results show that uncertainty estimates can add some reliability and reduce
sensitivity to classification threshold selection. While advanced metrics and
deep ensembles perform best in our comparison, the added value over simpler
metrics and TTA is small. Importantly, the benefit of all evaluated uncertainty
estimation methods is diminished by domain shift.



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