SF2Former: Amyotrophic lateral sclerosis identification from multi-center MRI data using spatial and frequency fusion transformer

计算机科学 人工智能 神经影像学 判别式 深度学习 模式识别(心理学) 机器学习 肌萎缩侧索硬化 医学 神经科学 心理学 病理 疾病
作者
Rafsanjany Kushol,Collin Luk,Avyarthana Dey,Michael Benatar,Hannah Briemberg,Annie Dionne,Nicolas Dupré,Richard Frayne,Angela Genge,Summer Gibson,Simon J. Graham,Lawrence Korngut,Peter Seres,Robert C. Welsh,Alan H. Wilman,Lorne Zinman,Sanjay Kalra,Yee‐Hong Yang
出处
期刊:Computerized Medical Imaging and Graphics [Elsevier BV]
卷期号:108: 102279-102279 被引量:7
标识
DOI:10.1016/j.compmedimag.2023.102279
摘要

Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder characterized by motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has emerged as a prominent class of machine learning algorithms in computer vision and has shown successful applications in various medical image analysis tasks. However, deep learning methods applied to neuroimaging have not achieved superior performance in classifying ALS patients from healthy controls due to insignificant structural changes correlated with pathological features. Thus, a critical challenge in deep models is to identify discriminative features from limited training data. To address this challenge, this study introduces a framework called SF2Former, which leverages the power of the vision transformer architecture to distinguish ALS subjects from the control group by exploiting the long-range relationships among image features. Additionally, spatial and frequency domain information is combined to enhance the network's performance, as MRI scans are initially captured in the frequency domain and then converted to the spatial domain. The proposed framework is trained using a series of consecutive coronal slices and utilizes pre-trained weights from ImageNet through transfer learning. Finally, a majority voting scheme is employed on the coronal slices of each subject to generate the final classification decision. The proposed architecture is extensively evaluated with multi-modal neuroimaging data (i.e., T1-weighted, R2*, FLAIR) using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of the proposed strategy in terms of classification accuracy compared to several popular deep learning-based techniques.
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
佛系少女发布了新的文献求助10
1秒前
夏夏发布了新的文献求助10
1秒前
隐形觅翠发布了新的文献求助10
1秒前
1秒前
量子星尘发布了新的文献求助10
1秒前
1秒前
帮帮我发布了新的文献求助10
2秒前
英姑应助苏黎世采纳,获得10
2秒前
Antony完成签到,获得积分10
3秒前
3秒前
chang完成签到,获得积分10
3秒前
3秒前
4秒前
喜欢玩辅助完成签到,获得积分10
4秒前
5秒前
乐乐应助小茉莉采纳,获得30
5秒前
今后应助周游采纳,获得20
5秒前
脑洞疼应助搞笑5次采纳,获得10
6秒前
6秒前
6秒前
hk完成签到,获得积分10
6秒前
wpr发布了新的文献求助10
6秒前
6秒前
Zz关闭了Zz文献求助
7秒前
7秒前
7秒前
7秒前
花开hhhhhhh发布了新的文献求助10
7秒前
7秒前
ads完成签到,获得积分10
7秒前
8秒前
陈成完成签到,获得积分10
8秒前
rose关注了科研通微信公众号
8秒前
8秒前
吃经济发布了新的文献求助10
9秒前
9秒前
9秒前
光_sun发布了新的文献求助10
10秒前
ldh发布了新的文献求助10
10秒前
10秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
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 3974559
求助须知:如何正确求助?哪些是违规求助? 3518949
关于积分的说明 11196503
捐赠科研通 3255066
什么是DOI,文献DOI怎么找? 1797673
邀请新用户注册赠送积分活动 877076
科研通“疑难数据库(出版商)”最低求助积分说明 806130