Sample selection with noise rate estimation in noise learning of medical
  image analysis
Maolin Li Giacomo Tarroni Vasilis Siomos
Deep learning techniques have demonstrated remarkable success in the field of
medical image analysis. However, the existence of label noise within data
significantly hampers its performance. In this paper, we introduce a novel
noise-robust learning method which integrates noise rate estimation into sample
selection approaches for handling noisy datasets. We first estimate the noise
rate of a dataset with Linear Regression based on the distribution of loss
values. Then, potentially noisy samples are excluded based on this estimated
noise rate, and sparse regularization is further employed to improve the
robustness of our deep learning model. Our proposed method is evaluated on five
benchmark medical image classification datasets, including two datasets
featuring 3D medical images. Experiments show that our method outperforms other
existing noise-robust learning methods, especially when noise rate is very big.