  Active Learning (AL) has the potential to solve a major problem of digital
pathology: the efficient acquisition of labeled data for machine learning
algorithms. However, existing AL methods often struggle in realistic settings
with artifacts, ambiguities, and class imbalances, as commonly seen in the
medical field. The lack of precise uncertainty estimations leads to the
acquisition of images with a low informative value. To address these
challenges, we propose Focused Active Learning (FocAL), which combines a
Bayesian Neural Network with Out-of-Distribution detection to estimate
different uncertainties for the acquisition function. Specifically, the
weighted epistemic uncertainty accounts for the class imbalance, aleatoric
uncertainty for ambiguous images, and an OoD score for artifacts. We perform
extensive experiments to validate our method on MNIST and the real-world Panda
dataset for the classification of prostate cancer. The results confirm that
other AL methods are 'distracted' by ambiguities and artifacts which harm the
performance. FocAL effectively focuses on the most informative images, avoiding
ambiguities and artifacts during acquisition. For both experiments, FocAL
outperforms existing AL approaches, reaching a Cohen's kappa of 0.764 with only
0.69% of the labeled Panda data.
