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一个修改的关系和基于边际的深度学习网络,用于自动检测乳腺癌.

Adyasha Sahu, Sukadev Meher

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    |August 14, 2025
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    概括
    此摘要是机器生成的。

    一种新的计算机辅助检测 (CAD) 模型,MReMarNet,提高了乳腺癌识别的准确性. 这种人工智能方法提高了使用乳房扫描和超声数据的早期检测,优于现有的方法.

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    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 在瘤学瘤学.

    背景情况:

    • 乳腺癌是妇女死亡的主要原因,需要改进早期检测方法.
    • 目前的查技术,如乳房影像和超声波,可以通过计算机辅助检测 (CAD) 系统来增强.
    • 对小样本数据集的准确分类仍然是诊断乳腺癌的挑战.

    研究的目的:

    • 引入一个修改后的关系和边际网络 (MReMarNet),以有效和准确地检测乳腺癌.
    • 提高医学成像中小样本数据集的分类性能.
    • 为了提高恶性瘤检测的类内紧性和类间分离性.

    主要方法:

    • 开发一个修改后的关系和边际网络 (MReMarNet),包括一个关系单元 (RU) 和一个完全连接的 (FC) 单元.
    • 通过RU同时应用特征学习和通过FC与交叉损失基于决策边界的分类.
    • 使用两个公共数据集进行实验验证:迷你DDSM (乳房图) 和BUSI (超声波).

    主要成果:

    • 拟议的MReMarNet实现了高准确率:小型DDSM的准确率为98.75%,BUSI的准确率为95.77%,BUS2.00的准确率为98.00%.
    • 该模型在乳腺癌识别方面表现优于其他网络.
    • 类内紧性和类间可分离性的综合优势有助于提高系统的效率.

    结论:

    • MReMarNet模型在计算机辅助乳腺癌检测方面取得了重大进展.
    • 拟议的方法有效地解决了在医学成像中分类小样本数据集的挑战.
    • MReMarNet显示了改善早期和准确的乳腺癌诊断的巨大潜力,可能降低死亡率.