OpenAL: An Efficient Deep Active Learning Framework for Open-Set
  Pathology Image Classification
Linhao Qu Yingfan Ma Zhiwei Yang Manning Wang Zhijian Song
Active learning (AL) is an effective approach to select the most informative
samples to label so as to reduce the annotation cost. Existing AL methods
typically work under the closed-set assumption, i.e., all classes existing in
the unlabeled sample pool need to be classified by the target model. However,
in some practical clinical tasks, the unlabeled pool may contain not only the
target classes that need to be fine-grainedly classified, but also non-target
classes that are irrelevant to the clinical tasks. Existing AL methods cannot
work well in this scenario because they tend to select a large number of
non-target samples. In this paper, we formulate this scenario as an open-set AL
problem and propose an efficient framework, OpenAL, to address the challenge of
querying samples from an unlabeled pool with both target class and non-target
class samples. Experiments on fine-grained classification of pathology images
show that OpenAL can significantly improve the query quality of target class
samples and achieve higher performance than current state-of-the-art AL
methods. Code is available at https://github.com/miccaiif/OpenAL.