OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification

TL;DR

This paper introduces OpenAL, a deep active learning framework designed for open-set pathology image classification, effectively selecting target samples from unlabeled pools containing irrelevant non-target classes, thereby improving classification performance.

Contribution

The paper presents a novel open-set active learning framework, OpenAL, specifically tailored for pathology images, addressing the challenge of non-target classes in unlabeled data.

Findings

OpenAL significantly improves query quality for target class samples.

OpenAL outperforms existing active learning methods in open-set pathology classification.

The framework enhances classification accuracy with fewer labeled samples.

Abstract

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.