  Deep neural networks are known to be data-driven and label noise can have a
marked impact on model performance. Recent studies have shown great robustness
to classic image recognition even under a high noisy rate. In medical
applications, learning from datasets with label noise is more challenging since
medical imaging datasets tend to have asymmetric (class-dependent) noise and
suffer from high observer variability.
  In this paper, we systematically discuss and define the two common types of
label noise in medical images - disagreement label noise from inconsistency
expert opinions and single-target label noise from wrong diagnosis record. We
then propose an uncertainty estimation-based framework to handle these two
label noise amid the medical image classification task. We design a
dual-uncertainty estimation approach to measure the disagreement label noise
and single-target label noise via Direct Uncertainty Prediction and
Monte-Carlo-Dropout.
  A boosting-based curriculum training procedure is later introduced for robust
learning. We demonstrate the effectiveness of our method by conducting
extensive experiments on three different diseases: skin lesions, prostate
cancer, and retinal diseases. We also release a large re-engineered database
that consists of annotations from more than ten ophthalmologists with an
unbiased golden standard dataset for evaluation and benchmarking.
