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<p>Tuberculosis (TB) remains a global health challenge, where chest X-rays (CXRs) are a primary tool for screening. Automated TB screening is complicated by the disease’s diverse and subtle visual manifestations. To address this, we propose FMoF, a novel deep learning framework that integrates frequency-domain information with spatial feature learning for robust CXR classification. Our core contributions include a frequency-enhanced attention module, which selectively enhances discriminative spectral components to enrich spatial feature maps, and a mixture-of-features module that dynamically aggregates multi-scale contextual representations. Through extensive benchmark evaluations on public datasets including TCX and TBX11K, our method achieves state-of-the-art performance, demonstrating superior accuracy and generalization. The proposed approach demonstrates the significant potential of hybrid domain reasoning for improving medical image analysis.</p>

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Enhancing representation learning with frequency-augmented feature mixture for robust tuberculosis screening

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Abudouresuli Tuersun,
  • Mireayi Tudi,
  • Abudoukeyoumu Abula,
  • Pahatijiang Nijiati,
  • Saimaitikari Abudoubari,
  • Feng Gao,
  • Xiaojian Wu,
  • Zekai Liu,
  • Lei Zhu,
  • Mayidili Nijiati

摘要

Tuberculosis (TB) remains a global health challenge, where chest X-rays (CXRs) are a primary tool for screening. Automated TB screening is complicated by the disease’s diverse and subtle visual manifestations. To address this, we propose FMoF, a novel deep learning framework that integrates frequency-domain information with spatial feature learning for robust CXR classification. Our core contributions include a frequency-enhanced attention module, which selectively enhances discriminative spectral components to enrich spatial feature maps, and a mixture-of-features module that dynamically aggregates multi-scale contextual representations. Through extensive benchmark evaluations on public datasets including TCX and TBX11K, our method achieves state-of-the-art performance, demonstrating superior accuracy and generalization. The proposed approach demonstrates the significant potential of hybrid domain reasoning for improving medical image analysis.