ArXiv Preprint
Pathology image analysis crucially relies on the availability and quality of
annotated pathological samples, which are very difficult to collect and need
lots of human effort. To address this issue, beyond traditional preprocess data
augmentation methods, mixing-based approaches are effective and practical.
However, previous mixing-based data augmentation methods do not thoroughly
explore the essential characteristics of pathology images, including the local
specificity, global distribution, and inner/outer-sample instance relationship.
To further understand the pathology characteristics and make up effective
pseudo samples, we propose the CellMix framework with a novel
distribution-based in-place shuffle strategy. We split the images into patches
with respect to the granularity of pathology instances and do the shuffle
process across the same batch. In this way, we generate new samples while
keeping the absolute relationship of pathology instances intact. Furthermore,
to deal with the perturbations and distribution-based noise, we devise a
loss-drive strategy inspired by curriculum learning during the training
process, making the model fit the augmented data adaptively. It is worth
mentioning that we are the first to explore data augmentation techniques in the
pathology image field. Experiments show SOTA results on 7 different datasets.
We conclude that this novel instance relationship-based strategy can shed light
on general data augmentation for pathology image analysis. The code is
available at https://github.com/sagizty/CellMix.
Tianyi Zhang, Zhiling Yan, Chunhui Li, Nan Ying, Yanli Lei, Yunlu Feng, Yu Zhao, Guanglei Zhang
2023-01-27
