喉Former:一个基于变压器的框架,用于处理和细分喉图像
Rune Mæstad1, Abdul Hanan1, Haakon Kristian Kvidaland2,3
1Faculty of Engineering and Science, Western Norway University of Applied Sciences, Bergen, Vestland, Norway.
Frontiers in digital health
|July 28, 2025
概括
本研究介绍了LarynxFormer,这是一种用于客观诊断运动诱导喉阻塞 (EILO) 的机器学习框架. 与传统方法相比,基于变压器的细分显著提高了诊断的准确性和速度.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 呼吸系统医学 呼吸系统医学
背景情况:
- 运动诱导喉阻塞 (EILO) 的手动诊断是主观的,容易产生人类偏见.
- 机器学习提供了客观的潜力,通过喉图像细分来实现EILO的自动诊断.
- 现有的细分方法需要对临床应用进行比较和改进.
研究的目的:
- 开发和评估一个新的机器学习框架,LarynxFormer,用于客观的EILO诊断.
- 为了比较基于变压器和基于卷积模型的喉图像细分的性能.
- 评估自动喉细分的诊断准确性和计算效率.
主要方法:
- 实施和训练了四种最先进的细分模型 (基于卷积和变压器) 在来自连续喉镜运动测试 (CLE测试) 的喉图像数据集上.
- 开发了一个新的框架,LarynxFormer,包括预处理,基于变压器的细分和后处理.
- 使用关键指标和计算速度比较模型性能.
主要成果:
- 拟议的LarynxFormer框架在喉图像细分方面表现出卓越的性能.
- 基于变压器的细分在准确性和效率方面明显超过了传统方法.
- 与其他方法相比,基于变压器的方法的推断时间高达2倍.
结论:
- 机器学习,特别是基于变压器的方法,显示出作为EILO客观诊断工具的重大前景.
- LarynxFormer提供了一种有效和高效的方法,用于自动化喉细分,推进EILO诊断.
- 这项研究强调了人工智能克服手动EILO评估局限性的潜力.
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