mlsanityData to 14 Oct 2026

MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis

arXiv 2306.17466 ↗First posted 30 Jun 2023eess.IV cs.CVcited by 4

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Abstract (arXiv)

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 the differences between natural and medical images. Extensive experimental results on four classification and four segmentation datasets demonstrate the superiority of MedAugment. Compared with existing approaches, the proposed MedAugment prevents producing color distortions or structural alterations while involving negligible computational overhead. Our method can serve as a plugin without an extra training stage, offering significant benefits to the community and medical experts lacking a deep learning foundation. The code is available at https://github.com/NUS-Tim/MedAugment.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 53 of its 87 references that are in the map. First posted on arXiv 30 Jun 2023.

Builds on

53 of 87 references.

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38 more
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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2025Revisiting Data Augmentation for Ultrasound Imagescited by 7
  2. 2024BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classificationcited by 5
  3. 2024Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Imagescited by 4
  4. 2024Segmenting Medical Images with Limited Datacited by 3