A Siamese-Transport Domain Adaptation Framework for 3D MRI Classification of Gliomas and Alzheimer's Diseases

计算机科学 人工智能 域适应 领域(数学分析) 胶质瘤 磁共振成像 医学 数学 放射科 癌症研究 分类器(UML) 数学分析
作者
Luyue Yu,Ju Liu,Qiang Wu,Jing Wang,Aixi Qu
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:28 (1): 391-402 被引量:4
标识
DOI:10.1109/jbhi.2023.3332419
摘要

Accurate and fully automated brain structure examination and prediction from 3D volumetric magnetic resonance imaging (MRI) is a necessary step in medical imaging analysis, which can assist greatly in clinical diagnosis. Traditional deep learning models suffer from severe performance degradation when applied to clinically acquired unlabeled data. The performance degradation is mainly caused by domain discrepancy such as different device types and parameter settings for data acquisition. However, existing approaches focus on the reduction of domain discrepancies but ignore the entanglement of semantic features and domain information. In this article, we explore the feature invariance of categories and domains in different projection spaces and propose a Siamese-Transport Domain Adaptation (STDA) method using a joint optimal transport theory and contrastive learning for automatic 3D MRI classification and glioma multi-grade prediction. Specifically, the learning framework updates the distribution of features across domains and categories by Siamese transport network training with an Optimal Cost Transfer Strategy (OCTS) and a Mutual Invariant Constraint (MIC) in two projective spaces to find multiple invariants in potential heterogeneity. We design three sets of transfer task scenarios with different source and target domains, and demonstrate that STDA yields substantially higher generalization performance than other state-of-the-art unsupervised domain adaptation (UDA) methods. The method is applicable on 3D MRI data from glioma to Alzheimer's disease and has promising applications in the future clinical diagnosis and treatment of brain diseases.
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
3秒前
阳光沛柔发布了新的文献求助10
3秒前
YYYZZX1完成签到,获得积分10
4秒前
英俊的铭应助Ljc采纳,获得10
4秒前
Rondab应助杜兰特工队采纳,获得30
5秒前
5秒前
5秒前
5秒前
隐形曼青应助yuanyuan采纳,获得10
5秒前
6秒前
nini发布了新的文献求助10
7秒前
思源应助zxunxia采纳,获得10
9秒前
9秒前
9秒前
9秒前
10秒前
量子星尘发布了新的文献求助30
10秒前
XYN1发布了新的文献求助10
10秒前
直立行走完成签到,获得积分10
10秒前
nini完成签到,获得积分10
12秒前
yuanyuan完成签到,获得积分20
13秒前
14秒前
14秒前
我晕豆芽发布了新的文献求助10
15秒前
16秒前
17秒前
沐风发布了新的文献求助10
17秒前
hh发布了新的文献求助10
18秒前
18秒前
Ljc完成签到,获得积分10
19秒前
昏睡的绿海完成签到,获得积分10
20秒前
21秒前
润泽发布了新的文献求助10
21秒前
22秒前
寻悦发布了新的文献求助10
24秒前
冷静的羿发布了新的文献求助10
24秒前
25秒前
25秒前
Ljc发布了新的文献求助10
26秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3988920
求助须知:如何正确求助?哪些是违规求助? 3531290
关于积分的说明 11253247
捐赠科研通 3269903
什么是DOI,文献DOI怎么找? 1804830
邀请新用户注册赠送积分活动 882027
科研通“疑难数据库(出版商)”最低求助积分说明 809052