Asymmetric dual thresholds and co-occurrence relationship pseudo label generation for partial multi-label task

In real clinical settings, medical image datasets are often partially annotated due to high labeling costs and complexity, which limits multi-label classification. Existing methods often attempt to tackle this challenge by either decoupling features to generate pseudo-labels or by treating all unknown labels as negative labels during training. In scenarios with severe label scarcity, the former approach may fail to generate high-quality decoupled features, while the latter is prone to introducing label noise. To address these challenges, we propose a novel method for partial multi-label medical image recognition tasks, called Asymmetric Dual Thresholds and Co-occurrence Relationship (ADTCR). Specifically, ADTCR consists of two pseudo-label generation strategies: Asymmetric Dual Threshold (ADT) and Co-occurrence Relationship (CR). The ADT strategy is designed to initially identify pseudo-labels by applying a lower threshold for negative pseudo labels and a higher threshold for positive pseudo labels, ensuring the generation of high-quality pseudo labels. Meanwhile, the CR strategy aims to uncover potential positive labels by capturing label co-occurrence relationships, enabling the detection of latent positive labels among the unknown ones. Finally, to assess the model’s confidence in the generated pseudo-labels, we design a Threshold-based Weighting Loss (TWL), which uses threshold-based weights to weight each pseudo-label, thereby further improving performance. Extensive experiments conducted on three multi-label medical image datasets, i.e., Axial Spondyloarthritis, NIH Chest X-ray 14, ODIR-5K, demonstrate that our method achieves state-of-the-art performance.