  Data augmentation (DA) has been widely leveraged in the realm of computer
vision to alleviate the data shortage, whereas the DA in medical image analysis
(MIA) faces multiple challenges. The prevalent DA approaches in MIA encompass
conventional DA, synthetic DA, and automatic DA. However, the utilization of
these approaches poses various challenges such as experience-driven design and
intensive computation cost. Here, we propose an efficient and effective
automatic DA method termed MedAugment. We propose the pixel augmentation space
and spatial augmentation space and exclude the operations that can break the
details and features within medical images. Besides, we propose a novel
sampling strategy by sampling a limited number of operations from the two
spaces. Moreover, we present a hyperparameter mapping relationship to produce a
rational augmentation level and make the MedAugment fully controllable using a
single hyperparameter. These revisions address the differences between natural
and medical images. Extensive experimental results on four classification and
three segmentation datasets demonstrate the superiority of MedAugment. We posit
that the plug-and-use and training-free MedAugment holds the potential to make
a valuable contribution to the medical field, particularly benefiting medical
experts lacking foundational expertise in deep learning. Code is available at
https://github.com/NUS-Tim/MedAugment.
