ArticleFrontiers in neuroscience2022
A pairwise functional connectivity similarity measure method based on few-shot learning for early MCI detection.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 18 citations in OpenAlex.
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- A fusion analytic framework for investigating functional brain connectivity differences using resting-state fMRI.Frontiers in neuroscience · 2024Article
- An Instance Segmentation Model Based on Deep Learning for Intelligent Diagnosis of Uterine Myomas in MRI.Diagnostics (Basel, Switzerland) · 2023Article
- A Similarity Measure-Based Approach Using RS-fMRI Data for Autism Spectrum Disorder Diagnosis.Diagnostics (Basel, Switzerland) · 2023Article
- DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era.Frontiers in medicine · 2023Article
- A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer.Frontiers in medicine · 2022Review
- From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks.Digital healthReview
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5 authors at 2 institutions in 1 country.
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Abstract
Alzheimer's disease is an irreversible neurological disease, therefore prompt diagnosis during its early stage, i.e., early mild cognitive impairment (MCI), is crucial for effective treatment. In this paper, we propose an automatic diagnosis method, a few-shot learning-based pairwise functional connectivity (FC) similarity measure method, to detect early MCI. We first employ a sliding window strategy to generate a dynamic functional connectivity network (FCN) using each subject's rs-fMRI data. Then, normal controls (NCs) and early MCI patients are distinguished by measuring the similarity between the dynamic FC series of corresponding brain regions of interest (ROIs) pairs in different subjects. However, previous studies have shown that FC patterns in different ROI-pairs contribute differently to disease classification. To enable the FCs of different ROI-pairs to make corresponding contributions to disease classification, we adopt a self-attention mechanism to weight the FC features. We evaluated the suggested strategy using rs-fMRI data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results point to the viability of our approach for detecting MCI at an early stage.
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