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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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基于ConvNeXtt的高精度宫癌前病变分类方法

Jing Tang¹, Ting Zhang², Zeyu Gong¹

¹State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

Bioengineering (Basel, Switzerland)

|December 23, 2023

概括

此摘要是机器生成的。

这项研究引入了一种使用ConvNeXt AI.用于宫癌查的新型高精度方法. 这种先进的技术提高了分类准确性,为癌前病变提供了更有效的诊断工具.

关键词:

宫癌查宫癌查深度学习是一种深度学习.基于液体的细胞学.

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科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

传统的宫癌诊断依赖于人类乳头瘤病毒 (HPV) 测试,由于个体变异和时间波动,这可能是昂贵的.
宫细胞学提供了一个替代方案,但由于复杂的细胞结构和微妙的亚型差异,在高精度查方面面临挑战.

研究的目的:

开发一种高精度的分类方法,用于宫癌前病变.
通过先进的人工智能技术来增强宫癌查.

主要方法:

基于ConvNeXt架构的新方法被提出.
自主监督的数据增强和集体学习策略用于特征提取和歧视.
深细胞学水平 (DCCL) 数据集,包括1167个宫细胞学标本,用于培训和验证.

主要成果:

拟议的方法在DCCL数据集上实现了比以前的先进模型高8.85%的分类准确度.
该技术在特征提取和宫细胞的类别间歧视方面表现出显著的优势.

结论:

基于ConvNeXt的方法在高精度的宫癌癌前癌性病变查方面取得了重大进展.
这种人工智能驱动的方法有望提高宫癌诊断的准确性和效率.