RankMix: Data Augmentation for Weakly Supervised Learning of Classifying Whole Slide Images with Diverse Sizes and Imbalanced Categories

Yuan-Chih Chen, Chun-Shien Lu

Abstract:

Whole Slide Images (WSIs) are usually gigapixel in size
and lack pixel-level annotations. The WSI datasets are also
imbalanced in categories. These unique characteristics,
significantly different from the ones in natural images, pose
the challenge of classifying WSI images as a kind of weakly
supervise learning problems. In this study, we propose,
RankMix, a data augmentation method of mixing ranked
features in a pair of WSIs. RankMix introduces the concepts
of pseudo labeling and ranking in order to extract key
WSI regions in contributing to the WSI classification task. A
two-stage training is further proposed to boost stable training
and model performance.
To our knowledge, the study of weakly supervised learning
from the perspective of data augmentation to deal with
the WSI classification problem that suffers from lack of
training data and imbalance of categories is relatively unexplored.