  Deep learning-based histopathology image classification is a key technique to
help physicians in improving the accuracy and promptness of cancer diagnosis.
However, the noisy labels are often inevitable in the complex manual annotation
process, and thus mislead the training of the classification model. In this
work, we introduce a novel hard sample aware noise robust learning method for
histopathology image classification. To distinguish the informative hard
samples from the harmful noisy ones, we build an easy/hard/noisy (EHN)
detection model by using the sample training history. Then we integrate the EHN
into a self-training architecture to lower the noise rate through gradually
label correction. With the obtained almost clean dataset, we further propose a
noise suppressing and hard enhancing (NSHE) scheme to train the noise robust
model. Compared with the previous works, our method can save more clean samples
and can be directly applied to the real-world noisy dataset scenario without
using a clean subset. Experimental results demonstrate that the proposed scheme
outperforms the current state-of-the-art methods in both the synthetic and
real-world noisy datasets. The source code and data are available at
https://github.com/bupt-ai-cz/HSA-NRL/.
