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This article is cited in 4 scientific papers (total in 4 papers)
IMAGE PROCESSING, PATTERN RECOGNITION
Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation
N. S. Davydovab, V. V. Evdokimovaab, P. G. Serafimovichab, V. I. Protsenkoab, A. G. Khramovab, A. V. Nikonorovab a Image Processing Systems Institute of the RAS - Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia, Samara
b Samara National Research University
Abstract:
Quality assessment and artifact detection in functional magnetic resonance imaging (fMRI) data is essential for clinical applications and brain research. Subject head motion remains the main source of artifacts - even the tiniest head movement can perturb the structural and functional data derived from the fMRI. In this paper, we propose an end-to-end neural network technology for detecting step anomalies with training on partially synthetic data with adaptation to a specific small set of real data. A procedure for generating a synthetic dataset for training and a module for automated labeling of real data is developed. A recurrent neural network model for detecting step anomalies is proposed. A method for the model adaptation to a small set of real data based on one-step meta-learning is developed. An experimental verification of the accuracy is carried out in the problem of detecting step anomalies using a sliding window of 10, 15, and 24 pixels. The experiments have shown the proposed technology to provide the detection of stepwise anomalies with an accuracy of 0.9546.
Keywords:
recurrent neural networks, anomaly detection, signal analysis, functional magnetic resonance imaging, meta-learning
Received: 11.05.2023 Accepted: 19.09.2023
Citation:
N. S. Davydov, V. V. Evdokimova, P. G. Serafimovich, V. I. Protsenko, A. G. Khramov, A. V. Nikonorov, “Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation”, Computer Optics, 47:6 (2023), 991–1001
Linking options:
https://www.mathnet.ru/eng/co1203 https://www.mathnet.ru/eng/co/v47/i6/p991
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