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Updated: May 23, 2025

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

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

Published on: April 21, 2023

使用高效NetB0进行甲状腺癌的高级病理亚型分类.

Hongpeng Guo¹, Junjie Zhang², You Li¹

¹Department of General Surgery, The Second Hospital Affiliated to Shenyang Medical College, No.64, Qishan West Road, Huanggu District, Shenyang, Liaoning, 110002, China.

Diagnostic pathology

|March 7, 2025

概括

此摘要是机器生成的。

这项研究表明,EfficientNetB0准确地识别了甲状腺癌亚型,并分析了瘤微环境特征. 这可以改善甲状腺癌患者的诊断和个性化治疗.

关键词:

有效NetB0算法模型模型病理学亚型病理学亚型个性化治疗个性化治疗准确的诊断,准确的诊断.甲状腺癌是什么?甲状腺癌是什么?瘤微环境是一个微环境.

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科学领域:

在瘤学瘤学.
人工智能的人工智能
生物信息学是一种生物信息学.

背景情况:

识别甲状腺癌亚型对于治疗和预后至关重要.
深度学习有助于瘤微环境分析,但其与结果的联系尚不清楚.

研究的目的:

评估用于甲状腺癌亚型分类的深度学习模型.
探索瘤微环境特征与临床结果之间的关系.

主要方法:

收集了来自118名甲状腺癌患者的病理,基因和蛋白质表达数据.
对比了10个AI模型,选择和验证了EfficientNetB0.0.
提取的微环境特征包括瘤免疫相互作用和ECM组成.

主要成果:

EfficientNetB0在区分甲状腺癌亚型 (乳头癌,卵泡癌,髓癌,形癌) 方面取得了很高的准确性.
该模型确定了微环境特征和子类型之间的显著相关性.
这些相关性影响疾病进展,治疗反应和预后.

结论:

EfficientNetB0有效地识别甲状腺癌亚型,并分析瘤微环境.
研究结果为准确的诊断和个性化的甲状腺癌治疗提供了洞察力.
通过澄清微环境-亚型关系,结果突出了潜在的分子标.