  In pathology image analysis, obtaining and maintaining high-quality annotated
samples is an extremely labor-intensive task. To overcome this challenge,
mixing-based methods have emerged as effective alternatives to traditional
preprocessing data augmentation techniques. Nonetheless, these methods fail to
fully consider the unique features of pathology images, such as local
specificity, global distribution, and inner/outer-sample instance
relationships. To better comprehend these characteristics and create valuable
pseudo samples, we propose the CellMix framework, which employs a novel
distribution-oriented in-place shuffle approach. By dividing images into
patches based on the granularity of pathology instances and shuffling them
within the same batch, the absolute relationships between instances can be
effectively preserved when generating new samples. Moreover, we develop a
curriculum learning-inspired, loss-driven strategy to handle perturbations and
distribution-related noise during training, enabling the model to adaptively
fit the augmented data. Our experiments in pathology image classification tasks
demonstrate state-of-the-art (SOTA) performance on 7 distinct datasets. This
innovative instance relationship-centered method has the potential to inform
general data augmentation approaches for pathology image classification. The
associated codes are available at https://github.com/sagizty/CellMix.
