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This is the research reproducibility paper for the ICPR 2024 paper “Learning Neural Networks for Multi-label Medical Image Retrieval Using Hamming Distance Fabricated with Jaccard Similarity Coefficient”. This work provides an overview of the problem, highlighting the motivation behind multi-label image retrieval in the medical domain. It delves into the algorithmic framework, offering a detailed explanation of the proposed solution, including illustrative examples. Additionally, we outline the implementation guidelines for replicating the approach. A thorough experimental evaluation emphasizes the employed loss functions, parameter analysis, and an ablation study. The limitations of this method are discussed at the end.

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Research Reproducibility Paper: Learning Neural Networks for Multi-label Medical Image Retrieval Using Hamming Distance Fabricated with Jaccard Similarity Coefficient

  • 会议论文

  • 发表日期：

    2026年

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  • Asim Manna,
  • Debdoot Sheet

摘要

This is the research reproducibility paper for the ICPR 2024 paper “Learning Neural Networks for Multi-label Medical Image Retrieval Using Hamming Distance Fabricated with Jaccard Similarity Coefficient”. This work provides an overview of the problem, highlighting the motivation behind multi-label image retrieval in the medical domain. It delves into the algorithmic framework, offering a detailed explanation of the proposed solution, including illustrative examples. Additionally, we outline the implementation guidelines for replicating the approach. A thorough experimental evaluation emphasizes the employed loss functions, parameter analysis, and an ablation study. The limitations of this method are discussed at the end.