MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis
Data augmentation (DA) has been widely leveraged in 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, utilizing these approac...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
30.06.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Data augmentation (DA) has been widely leveraged in 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, utilizing 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 a pixel augmentation space
and spatial augmentation space and exclude the operations that can break
medical details and features, such as severe color distortions or structural
alterations that can compromise image diagnostic value. 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 configurations settle the differences between
natural and medical images, such as high sensitivity to certain attributes such
as brightness and posterize. Extensive experimental results on four
classification and four segmentation datasets demonstrate the superiority of
MedAugment. Compared with existing approaches, the proposed MedAugment serves
as a more suitable yet general processing pipeline for medical images without
producing color distortions or structural alterations and involving negligible
computational overhead. We emphasize that our method can serve as a plugin for
arbitrary projects without any extra training stage, thereby holding the
potential to make a valuable contribution to the medical field, particularly
for medical experts without a solid foundation in deep learning. Code is
available at https://github.com/NUS-Tim/MedAugment. |
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DOI: | 10.48550/arxiv.2306.17466 |