TL;DR
MedAugment is a universal automatic data augmentation plugin designed specifically for medical image analysis, improving performance while avoiding distortions and reducing computational costs.
Contribution
The paper introduces MedAugment, a general automatic data augmentation method tailored for medical images, with a novel sampling strategy and hyperparameter control for safe and efficient augmentation.
Findings
Outperforms existing augmentation methods on multiple datasets
Prevents color distortions and structural alterations in medical images
Operates with negligible computational overhead
Abstract
Data augmentation (DA) has been widely leveraged in computer vision to alleviate data shortage, while its application in medical imaging faces multiple challenges. The prevalent DA approaches in medical image analysis encompass conventional DA, synthetic DA, and automatic DA. However, these approaches may result in experience-driven design and intensive computation costs. Here, we propose a suitable yet general automatic DA method for medical images termed MedAugment. We propose pixel and spatial augmentation spaces and exclude the operations that can break medical details and features. Besides, we propose a 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…
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