Purpose: Deep neural networks (DNNs) have been widely applied in medical
image classification, benefiting from its powerful mapping capability among
medical images. However, these existing deep learning-based methods depend on
an enormous amount of carefully labeled images. Meanwhile, noise is inevitably
introduced in the labeling process, degrading the performance of models. Hence,
it's significant to devise robust training strategies to mitigate label noise
in the medical image classification tasks. Methods: In this work, we propose a
novel Bayesian statistics guided label refurbishment mechanism (BLRM) for DNNs
to prevent overfitting noisy images. BLRM utilizes maximum a posteriori
probability (MAP) in the Bayesian statistics and the exponentially
time-weighted technique to selectively correct the labels of noisy images. The
training images are purified gradually with the training epochs when BLRM is
activated, further improving classification performance. Results: Comprehensive
experiments on both synthetic noisy images (public OCT & Messidor datasets) and
real-world noisy images (ANIMAL-10N) demonstrate that BLRM refurbishes the
noisy labels selectively, curbing the adverse effects of noisy data. Also, the
anti-noise BLRM integrated with DNNs are effective at different noise ratio and
are independent of backbone DNN architectures. In addition, BLRM is superior to
state-of-the-art comparative methods of anti-noise. Conclusions: These
investigations indicate that the proposed BLRM is well capable of mitigating
label noise in medical image classification tasks.Comment: 10 pages, 11 figure