  Deep learning models are notoriously data-hungry. Thus, there is an urging
need for data-efficient techniques in medical image analysis, where
well-annotated data are costly and time consuming to collect. Motivated by the
recently revived "Copy-Paste" augmentation, we propose TumorCP, a simple but
effective object-level data augmentation method tailored for tumor
segmentation. TumorCP is online and stochastic, providing unlimited
augmentation possibilities for tumors' subjects, locations, appearances, as
well as morphologies. Experiments on kidney tumor segmentation task demonstrate
that TumorCP surpasses the strong baseline by a remarkable margin of 7.12% on
tumor Dice. Moreover, together with image-level data augmentation, it beats the
current state-of-the-art by 2.32% on tumor Dice. Comprehensive ablation studies
are performed to validate the effectiveness of TumorCP. Meanwhile, we show that
TumorCP can lead to striking improvements in extremely low-data regimes.
Evaluated with only 10% labeled data, TumorCP significantly boosts tumor Dice
by 21.87%. To the best of our knowledge, this is the very first work exploring
and extending the "Copy-Paste" design in medical imaging domain. Code is
available at: https://github.com/YaoZhang93/TumorCP.
