Robust Medical Image Classification From Noisy Labeled Data With Global and Local Representation Guided Co-Training

@article{Xue2022RobustMI,
  title={Robust Medical Image Classification From Noisy Labeled Data With Global and Local Representation Guided Co-Training},
  author={Cheng Xue and Lequan Yu and Pengfei Chen and Qi Dou and Pheng-Ann Heng},
  journal={IEEE Transactions on Medical Imaging},
  year={2022},
  volume={41},
  pages={1371-1382}
}
Deep neural networks have achieved remarkable success in a wide variety of natural image and medical image computing tasks. However, these achievements indispensably rely on accurately annotated training data. If encountering some noisy-labeled images, the network training procedure would suffer from difficulties, leading to a sub-optimal classifier. This problem is even more severe in the medical image analysis field, as the annotation quality of medical images heavily relies on the expertise… 
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