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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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医疗视觉语言预培训的本地调整

Huimin Yan, Xian Yang, Liang Bai

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |November 10, 2025
    PubMed
    概括

    通过准确对齐图像和报告,MedAligner 改善了医学视觉语言预训练. 它精确地识别病变和相关文本,以获得更好的语义对应.

    科学领域:

    • 人工智能的人工智能
    • 医疗成像医学成像
    • 自然语言处理自然语言处理.

    背景情况:

    • 在视觉语言预训练中,建立医学图像和报告之间的语义对应关系至关重要.
    • 现有的方法与小/模糊的损伤区域和杂的医疗报告作斗争,阻碍了准确的对齐.

    研究的目的:

    • 介绍MedAligner,一个旨在加强医疗视觉语言预培训中的本地语义对齐的新型网络.
    • 为应对由图像特征引起的损伤局部化和语义对齐方面的挑战,并报告冗余性.

    主要方法:

    • MedAligner使用双编码器进行全球和本地表示,并使用全球对比学习来实现语义一致性.
    • 引入了 Word-Region Alignment 具有可学习的,分散的词像素相似性矩阵,用于准确的损伤识别.
    • 纳入诊断术语过以关注高度重要的术语,并使用本地对比损失将其与病变对齐.
    • 采用渐进式培训策略,包括报告重建和动态相似性更新,以精制图像-文本对.

    主要成果:

    • 在短语接地,图像-文本检索和零拍摄分类方面,MedAligner显著优于现有的方法.
    • 在医学图像和报告之间建立准确的本地语义对应的过程中表现出卓越的性能.
    • 制定了医疗视觉语言预培训任务的新基准.

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    结论:

    • MedAligner有效地克服了当前医疗视觉语言预培训方法的局限性.
    • 提出的方法提高了病变定位和语义对齐的精度.
    • 这项工作通过改进视觉和文本数据的整合来推动医疗AI领域的发展.