Open-Set Active Learning for Nucleus Detection From the Histopathological Images

The recent advance of deep learning has shown great potential for nucleus detection which plays an important role in the histopathological examination. However, such accurate and reliable deep learning models usually need enough labeled data for training, which makes active learning an appealing learning paradigm to reduce the annotation efforts by experts. In open-set environments, active learning encounters the challenge that the unlabeled data usually contain non-target samples from the unknown classes, resulting in the failure of most active learning methods. Although active learning has been explored in many open-set classification tasks, research on active learning for nucleus detection in the open-set environment remains unexplored. To address the above issues, we propose a two-stage active learning framework designed for nucleus detection in the open-set environment (i.e., OpAL4ND). In the first stage, we propose a prototype-based query strategy based on the auxiliary detector to select a candidate set from known classes as pure as possible. In the second stage, we further query the most uncertain and representative samples from the candidate set for the nucleus detection task relying on the target detector. We evaluate the performance of our method on two nucleus detection datasets (i.e., the NuCLS and PanNuke datasets), and the experimental results indicate that our method can not only improve the selection quality on the known classes, but also achieve higher detection accuracy with lower annotation burden in comparison with the existing studies. Code is available at https://github.com/onbut/OpAL4ND.