  Pancreatic cancers have one of the worst prognoses compared to other cancers,
as they are diagnosed when cancer has progressed towards its latter stages. The
current manual histological grading for diagnosing pancreatic adenocarcinomas
is time-consuming and often results in misdiagnosis. In digital pathology,
AI-based cancer grading must be extremely accurate in prediction and
uncertainty quantification to improve reliability and explainability and are
essential for gaining clinicians trust in the technology. We present Bayesian
Convolutional Neural Networks for automated pancreatic cancer grading from MGG
and HE stained images to estimate uncertainty in model prediction. We show that
the estimated uncertainty correlates with prediction error. Specifically, it is
useful in setting the acceptance threshold using a metric that weighs
classification accuracy-reject trade-off and misclassification cost controlled
by hyperparameters and can be employed in clinical settings.
