Co-Pseudo Labeling and Active Selection for Fundus Single-Positive Multi-Label Learning

Due to the difficulty of collecting multi-label annotations for retinal diseases, fundus images are usually annotated with only one label, while they actually have multiple labels. Given that deep learning requires accurate training data, incomplete disease information may lead to unsatisfactory classifiers and even misdiagnosis. To cope with these challenges, we propose a co-pseudo labeling and active selection method for Fundus Single-Positive multi-label learning, named FSP. FSP trains two networks simultaneously to generate pseudo labels through curriculum co-pseudo labeling and active sample selection. The curriculum co-pseudo labeling adjusts the thresholds according to the model's learning status of each class. Then, the active sample selection maintains confident positive predictions with more precise pseudo labels based on loss modeling. A detailed experimental evaluation is conducted on seven retinal datasets. Comparison experiments show the effectiveness of FSP and its superiority over previous methods. Downstream experiments are also presented to validate the proposed method.