Self-supervised driven consistency training for annotation efficient histopathology image analysis

•We design a self-supervised pretext task via predicting the resolution sequence ordering in histology WSI.•We propose a teacher-student consistency paradigm to effectively transfer the pretrained representations to downstream tasks.•Extensive validation experiments on three histopathology benchmark datasets for classification and regression based tasks.•Proposed method yields tangible improvements outperforming other state-of-the-art self-supervised and supervised baselines.