  The challenge of addressing mixed closed-set and open-set label noise in
medical image classification remains largely unexplored. Unlike natural image
classification where there is a common practice of segregation and separate
processing of closed-set and open-set noisy samples from clean ones, medical
image classification faces difficulties due to high inter-class similarity
which complicates the identification of open-set noisy samples. Moreover,
prevailing methods do not leverage the full potential of open-set noisy samples
for label noise mitigation, often leading to their exclusion or application of
uniform soft labels. To address these issues, we propose an Extended
Noise-robust Contrastive and Open-set Feature Augmentation (ENCOFA) framework.
ENCOFA includes the Extended Noise-robust Supervised Contrastive (ENSC) Loss,
which aids in distinguishing features across classes. The ENSC loss regards
open-set noisy samples as an extended class and mitigates label noise by
weighting contrastive pairs with label reliability. Furthermore, we develop an
Open-set Feature Augmentation (OSFeatAug) module that enriches the features of
open-set samples, utilizing the model's extra capacity to prevent overfitting
to noisy data. We conducted experiments on a synthetic noisy dataset and a
real-world noisy dataset. Our results indicate the superiority of ENCOFA and
the effectiveness of leveraging the open-set noisy samples to combat label
noise.
