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SuperpixelGridMasks Data Augmentation: Application to Precision Health and Other Real-world Data
Karim Hammoudi1,2, Adnane Cabani3, Bouthaina Slika4,5,6
1IRIMAS, Université de Haute-Alsace, Mulhouse, France.
Journal of Healthcare Informatics Research
|January 23, 2023
Summary
SuperpixelGridMasks, a novel data augmentation technique, enhances machine learning model performance by transforming image datasets. This method, with variants SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix, outperforms existing data augmentation strategies.
Area of Science:
- Computer Vision
- Machine Learning
- Data Augmentation
Background:
- Machine learning models require extensive datasets for optimal performance.
- Existing data augmentation techniques may not fully leverage image data characteristics.
Purpose of the Study:
- To introduce SuperpixelGridMasks, a novel data augmentation approach for enhancing machine learning model performance.
- To present three variants: SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix.
Main Methods:
- Irregular superpixel decomposition for data augmentation.
- Grid-based image transformations involving information dropping and fusing.
- Implementation of SuperpixelGridCut, SuperpixelGridMean, and SuperpixelGridMix variants.
Main Results:
- SuperpixelGridMasks significantly outperformed baseline performances across various image classification models.
- The proposed methods surpassed the performance of other existing data augmentation techniques.
- Demonstrated effectiveness on precision health and real-world datasets.
Conclusions:
- SuperpixelGridMasks offers a powerful new approach to data augmentation in machine learning.
- The method's variants provide versatile tools for improving model accuracy and robustness.
- Publicly available code facilitates adoption and further research.
