Semi‐supervised graph convolutional networks for the domain adaptive recognition of thyroid nodules in cross‐device ultrasound images

卷积神经网络 人工智能 计算机科学 模式识别(心理学) 甲状腺结节 图形 域适应 深度学习 领域(数学分析) 计算机辅助诊断 计算机视觉 分类器(UML) 甲状腺 医学 数学 数学分析 内科学 理论计算机科学
作者
Kun Zhang,Zhongyu Li,Cai Chang,Jingyi Liu,Dou Xu,Chaowei Fang,Peng Huang,Ying Wang,Meng Yang,Shi Chang
出处
期刊:Medical Physics [Wiley]
卷期号:50 (12): 7806-7821 被引量:2
标识
DOI:10.1002/mp.16384
摘要

Ultrasound plays a critical role in the early screening and diagnosis of cancers. Although deep neural networks have been widely investigated in the computer-aided diagnosis (CAD) of different medical images, diverse ultrasound devices, and image modalities pose challenges for clinical applications, especially in the recognition of thyroid nodules having various shapes and sizes. More generalized and extensible methods need to be developed for the cross-devices recognition of thyroid nodules.In this work, a semi-supervised graph convolutional deep learning framework is proposed for the domain adaptative recognition of thyroid nodules across several ultrasound devices. A deep classification network, trained on a source domain with a specific device, can be transferred to recognize thyroid nodules on the target domain with other devices, using only few manual annotated ultrasound images.This study presents a semi-supervised graph-convolutional-network-based domain adaptation framework, namely Semi-GCNs-DA. Based on the ResNet backbone, it is extended in three aspects for domain adaptation, that is, graph convolutional networks (GCNs) for the connection construction between source and target domains, semi-supervised GCNs for accurate target domain recognition, and pseudo labels for unlabeled target domains. Data were collected from 1498 patients comprising 12 108 images with or without thyroid nodules under three different ultrasound devices. Accuracy, Sensitivity and Specificity were used for the performance evaluation.The proposed method was validated on six groups of data for a single source domain adaptation task, the mean Accuracy was 0.9719 ± 0.0023, 0.9928 ± 0.0022, 0.9353 ± 0.0105, 0.8727 ± 0.0021, 0.7596 ± 0.0045, 0.8482 ± 0.0092, which achieved better performance in comparison with the state-of-the-art. The proposed method was also validated on three groups of multiple source domain adaptation tasks. In particular, when using X60 and HS50 as the source domain data, and H60 as the target domain, it can achieve the Accuracy of 0.8829 ± 0.0079, Sensitivity of 0.9757 ± 0.0001, and Specificity of 0.7894 ± 0.0164. Ablation experiments also demonstrated the effectiveness of the proposed modules.The developed Semi-GCNs-DA framework can effectively recognize thyroid nodules on different ultrasound devices. The developed semi-supervised GCNs can be further extended to the domain adaptation problems for other modalities of medical images.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
芳泽发布了新的文献求助10
2秒前
su发布了新的文献求助10
3秒前
Milou完成签到,获得积分10
4秒前
4秒前
老阎应助科研通管家采纳,获得30
4秒前
orixero应助科研通管家采纳,获得10
5秒前
汉堡包应助科研通管家采纳,获得10
5秒前
5秒前
小马甲应助科研通管家采纳,获得10
5秒前
科研白菜白完成签到,获得积分10
5秒前
慕青应助科研通管家采纳,获得10
5秒前
大模型应助科研通管家采纳,获得10
5秒前
无花果应助科研通管家采纳,获得10
5秒前
英俊的铭应助科研通管家采纳,获得20
5秒前
5秒前
SciGPT应助科研通管家采纳,获得10
5秒前
科研乞丐应助科研通管家采纳,获得20
5秒前
jjj应助科研通管家采纳,获得20
5秒前
小二郎应助科研通管家采纳,获得10
5秒前
小马甲应助科研通管家采纳,获得30
5秒前
桐桐应助科研通管家采纳,获得10
5秒前
ding应助科研通管家采纳,获得10
5秒前
5秒前
烟花应助科研通管家采纳,获得10
5秒前
5秒前
5秒前
5秒前
充电宝应助科研通管家采纳,获得10
5秒前
zpt完成签到,获得积分10
6秒前
爱学习的瑞瑞子完成签到 ,获得积分10
6秒前
pauchiu完成签到,获得积分0
6秒前
jay完成签到,获得积分10
6秒前
7秒前
8秒前
9秒前
xixi完成签到 ,获得积分10
9秒前
杜熙完成签到,获得积分10
9秒前
11秒前
12秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038619
求助须知:如何正确求助?哪些是违规求助? 3576294
关于积分的说明 11375058
捐赠科研通 3306084
什么是DOI,文献DOI怎么找? 1819374
邀请新用户注册赠送积分活动 892698
科研通“疑难数据库(出版商)”最低求助积分说明 815066