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<p>In medical image analysis, acquiring large-scale labeled datasets remains challenging, and images often exhibit high overall similarity that requires expert-level interpretation, differing substantially from natural image processing. To address these issues, we introduce the Information-Gated Memory (IGM) unit, a memory mechanism that enables deep networks to store and compare category-specific information. Unlike traditional CNNs or RNNs, the IGM unit performs memory-guided contrastive matching, allowing the network to focus on diagnostically relevant features and enhance classification performance. Using a CBCT dataset of 392 individuals, divided according to the presence or absence of artifacts, the proposed IGMNN achieved classification accuracies of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(97.3\%\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(93.9\%\)</EquationSource> </InlineEquation>, respectively.</p>

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IGMNN: a diagnosis method for vertical root fractures based on an information gated memory neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Jie Wang,
  • Xinyan Jin,
  • Ruimin Tang,
  • Zhonglin Zeng,
  • Zitong Lin,
  • Yikun Li,
  • Ying Chen

摘要

In medical image analysis, acquiring large-scale labeled datasets remains challenging, and images often exhibit high overall similarity that requires expert-level interpretation, differing substantially from natural image processing. To address these issues, we introduce the Information-Gated Memory (IGM) unit, a memory mechanism that enables deep networks to store and compare category-specific information. Unlike traditional CNNs or RNNs, the IGM unit performs memory-guided contrastive matching, allowing the network to focus on diagnostically relevant features and enhance classification performance. Using a CBCT dataset of 392 individuals, divided according to the presence or absence of artifacts, the proposed IGMNN achieved classification accuracies of \(97.3\%\) and \(93.9\%\) , respectively.