OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
This addresses a practical challenge in clinical pathology by enabling more efficient annotation in open-set environments, though it is an incremental improvement over existing active learning methods.
The paper tackles the problem of active learning in open-set scenarios where unlabeled data includes irrelevant non-target classes, proposing OpenAL to improve query quality for target classes in pathology image classification, achieving higher performance than state-of-the-art methods.
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.