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单视图对比学习用于喉白血症分类与NBI喉镜图像

Zhenzhen You¹, Botao Han¹, Zhenghao Shi¹

¹Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.

|April 28, 2025

概括

这项研究引入了一种新的单视图对比学习网络,用于使用NBI图像精确地分类喉白血病. 开发的模型达到96.12%的准确性,在早期喉癌诊断中表现优于现有的方法.

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:

喉癌是一种常见的上呼吸道恶性瘤,需要早期和准确的诊断,以改善患者的治疗结果.
用窄带成像 (NBI) 进行喉腔镜检查有助于内镜医生,但从NBI图像中精细分类喉白血病仍然是计算机辅助诊断的挑战.
开发强大的AI模型对于提高具有挑战性的病例的诊断精度至关重要.

研究的目的:

开发一种计算机辅助诊断系统,使用NBI图像对喉白血症进行细分分类.
通过有效的学习策略,解决医疗图像数据集中小样本大小的挑战.
为了提高喉癌诊断的准确性和效率.

主要方法:

提出了一个单视图对比学习网络,结合了病变本地化和未标记数据的伪标签.
在原始NBI图像上预训练了一个骨干网络,然后使用注意力引导网络进行补丁生成以增强样本.
脊柱和对比学习网络的联合训练是使用组合的对比和交叉损失函数进行的.

主要成果:

拟议的模型在NBI数据集上实现了96.12%的高精度,超过了当前的主流模型.
该模型在分类六个类别时表现出高的特异性和灵敏性:正常组织,炎症性角质炎症,轻度,中度,重度发育不良和状细胞癌.
实验验证证了该模型在精细分类任务中的有效性.

结论:

开发的单视图对比学习网络为精确和高效的喉白血病细分类提供了一个有前途的方法.
该模型的卓越性能凸显了其在帮助临床医生早期诊断喉癌方面的潜力.
该代码的可用性有助于进一步研究和开发人工智能驱动的医学诊断.

关键词:

注意引导机制的注意力引导机制计算机辅助诊断是指计算机辅助的诊断.相反的学习学习学习.窄带成像技术的使用.半监督学习半监督学习

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