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<p>The segmentation of medical images plays a crucial role in diagnoses, treatment planning, and clinical decision-making. The performance of segmentation models is affected by several reasons, including a lack of labeled medical images and misinterpretation of image features, which affects accuracy. This paper introduces a new technique, Dynamic Contextual Feature Activation (DFCA), that dynamically selects and suppresses image features based on their contextual importance. Two models are presented; DFCA-GAN incorporates DFCA in Generative Adversarial Networks (GAN) to generate high-quality synthetic medical images and masks for data augmentation. DFCA-U-Net is an improved version of U-Net that uses DFCA to emphasize critical regions in image segmentation. The experiments on the ISIC 2016, ISIC 2017 skin lesion datasets, and the Chest X-ray Dataset for Tuberculosis demonstrate that DFCA-U-Net with DFCA-GAN data augmentation outperforms traditional U-Net and DFCA-U-Net, achieving 90.42%, 90.22%, and 79.02% in the Dice coefficient. This study demonstrates that DFCA improves deep learning models and data augmentation increases performance by reducing data scarcity, thereby enhancing the robustness and generalizability of the model.</p>

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Dynamic feature context activation and data augmentation for enhanced medical image segmentation

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • Khadija Rais,
  • Mohamed Amroune,
  • Mohamed Yassine Haouam,
  • Abdelmadjid Benmachiche,
  • Safa Abid

摘要

The segmentation of medical images plays a crucial role in diagnoses, treatment planning, and clinical decision-making. The performance of segmentation models is affected by several reasons, including a lack of labeled medical images and misinterpretation of image features, which affects accuracy. This paper introduces a new technique, Dynamic Contextual Feature Activation (DFCA), that dynamically selects and suppresses image features based on their contextual importance. Two models are presented; DFCA-GAN incorporates DFCA in Generative Adversarial Networks (GAN) to generate high-quality synthetic medical images and masks for data augmentation. DFCA-U-Net is an improved version of U-Net that uses DFCA to emphasize critical regions in image segmentation. The experiments on the ISIC 2016, ISIC 2017 skin lesion datasets, and the Chest X-ray Dataset for Tuberculosis demonstrate that DFCA-U-Net with DFCA-GAN data augmentation outperforms traditional U-Net and DFCA-U-Net, achieving 90.42%, 90.22%, and 79.02% in the Dice coefficient. This study demonstrates that DFCA improves deep learning models and data augmentation increases performance by reducing data scarcity, thereby enhancing the robustness and generalizability of the model.