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.
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