Clean Label Disentangling for Medical Image Segmentation with Noisy
  Labels
Zicheng Wang Zhen Zhao Erjian Guo Luping Zhou
Current methods focusing on medical image segmentation suffer from incorrect
annotations, which is known as the noisy label issue. Most medical image
segmentation with noisy labels methods utilize either noise transition matrix,
noise-robust loss functions or pseudo-labeling methods, while none of the
current research focuses on clean label disentanglement. We argue that the main
reason is that the severe class-imbalanced issue will lead to the inaccuracy of
the selected ``clean'' labels, thus influencing the robustness of the model
against the noises. In this work, we come up with a simple but efficient
class-balanced sampling strategy to tackle the class-imbalanced problem, which
enables our newly proposed clean label disentangling framework to successfully
select clean labels from the given label sets and encourages the model to learn
from the correct annotations. However, such a method will filter out too many
annotations which may also contain useful information. Therefore, we further
extend our clean label disentangling framework to a new noisy feature-aided
clean label disentangling framework, which takes the full annotations into
utilization to learn more semantics. Extensive experiments have validated the
effectiveness of our methods, where our methods achieve new state-of-the-art
performance. Our code is available at https://github.com/xiaoyao3302/2BDenoise.