Abstract
Diabetic retinopathy (DR) remains the most prevalent cause of vision impairment and irreversible blindness in the working-age adults. Due to the renaissance of deep learning (DL), DL-based DR diagnosis has become a promising tool for the early screening and severity grading of DR. However, training deep neural networks (DNNs) requires an enormous amount of carefully labeled data. Noisy label data may be introduced when labeling plenty of data, degrading the performance of models. In this work, we propose a novel label management mechanism (LMM) for the DNN to overcome overfitting on the noisy data. LMM utilizes maximum posteriori probability (MAP) in the Bayesian statistic and time-weighted technique to selectively correct the labels of unclean data, which gradually purify the training data and improve classification performance. Comprehensive experiments on both synthetic noise data (Messidor \& our collected DR dataset) and real-world noise data (ANIMAL-10N) demonstrated that LMM could boost performance of models and is superior to three state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2106.12284
