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<p>In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix. These are applied at different ratios for each class to adaptively learn features for each class. This method is tailored to handle the unique characteristics of each FLL type by adjusting the augmentation mix for major classes (e.g., cysts and metastases) and minor classes (e.g., hemangiomas). In experiments, our method was validated on the dataset consisted of portal phase CT images from 1290 colorectal cancer patients. The results showed that applying MixUp and AugMix in a class-wise manner could significantly improve the classification performance of minor classes while maintaining or slightly improving the performance of major classes. Quantitative results showed higher F1 scores for minor classes and a more balanced accuracy across classes when CCDA was used compared to other methods.</p>

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Class-Wise Combination of Mixture-Based Data Augmentation for Class Imbalance Learning of Focal Liver Lesions in Abdominal CT Images

  • 期刊论文

  • 发表日期：

    2025年1月27日

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  • Hansang Lee,
  • Deokseon Kim,
  • Joonseok Lim,
  • Helen Hong

摘要

In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix. These are applied at different ratios for each class to adaptively learn features for each class. This method is tailored to handle the unique characteristics of each FLL type by adjusting the augmentation mix for major classes (e.g., cysts and metastases) and minor classes (e.g., hemangiomas). In experiments, our method was validated on the dataset consisted of portal phase CT images from 1290 colorectal cancer patients. The results showed that applying MixUp and AugMix in a class-wise manner could significantly improve the classification performance of minor classes while maintaining or slightly improving the performance of major classes. Quantitative results showed higher F1 scores for minor classes and a more balanced accuracy across classes when CCDA was used compared to other methods.