Batch mode active learning for mitotic phenotypes using conformal prediction

Machine learning models are now ubiquitous in all areas of data analysis. As the amount of data generated continues to increase exponentially, the task of annotating sufficient objects with known labels by an expert remains expensive. To mitigate this, active learning approaches attempt to identify those objects whose labels will be most informative. Here, we introduce a batch-based active learning framework in a pooled setting based around conformal predictors. We select objects to add to the labelled observations based on perceived novelty, while mitigating the risks of selecting highly correlated or outlying observations. We compare our approach to classical methods using an example UCI dataset, and demonstrate its application to a pharmaceutically relevant cellular imaging problem for classifying mitotic phenotypes. Our approach facilitates efficient discovery of rare and novel classes within large screening datasets.