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不确定性驱动的基于并行变压器的口腔疾病数据集细分.

Lintao Peng, Wenhui Liu, Siyu Xie

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |March 4, 2025
    PubMed
    概括

    Oralformer是一个新型网络,通过结合局部窗口自我注意力和通道智能卷积,准确地细分多种口腔疾病. 这种方法提高了细分的准确性,特别是对于具有挑战性的损伤边界,并引入了一个大规模的数据集,以推进口腔健康研究.

    科学领域:

    • 医学图像分析 医学图像分析
    • 计算机视觉 计算机视觉
    • 口腔病理学 口腔病理学

    背景情况:

    • 正确的口腔疾病细分受到疾病变异性,模糊的病变边界和有限的公共数据集的阻碍.
    • 现有的方法与口腔病变的多样化视觉特征和模两可的边缘作斗争.

    研究的目的:

    • 开发一种先进的深度学习模型,用于准确细分多种口腔疾病.
    • 引入一种新的网络架构和不确定性驱动的损失函数,以提高细分性能.
    • 创建和发布一个大规模的数据集,用于口腔疾病细分研究.

    主要方法:

    • 开发了Oralformer,一个U形的编码器解码器网络,使用并行LC块结合本地窗口自我注意 (LWSA) 和通道智能卷积 (CWC).
    • 实施了不确定性驱动的自我适应性损失函数,以改善对模两可的损伤边界的关注.
    • 构建了一个大规模的口腔疾病细分 (ODS) 数据集,包含2602个图像对,涵盖斑块,结石和.

    主要成果:

    • Oralformer在六个具有挑战性的数据集中实现了最先进的细分精度.
    • 以每秒35的速度展示了卓越的概括性和实时细分效率.
    • 不确定性驱动的损失函数有效地改善了难以识别的损伤边缘的细分.

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    结论:

    • Oralformer在口腔疾病自动细分方面取得了重大进展.
    • 预计开发的方法和数据集将加速口腔健康领域的研究和临床应用.
    • 公共可用的代码和数据集有助于进一步开发和验证.