3D Deformable Convolutions for MRI classification

Pominova, Marina; Kondrateva, Ekaterina; Sharaev, Maxim; Pavlov, S. G.; Bernstein, Alexander; Burnaev, Evgeny

doi:10.48550/arxiv.1911.01898

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Application Of Autoencoder Based Models For Eeg Recordings1

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2023

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“…As in the previous case, the spectrogram is used as input and as ground truth data. Furthermore, there is an approach that utilizes statistical features of EEG recordings [5]. For each EEG channel FFT is applied.…”

Section: Application Of Autoencoder Based Models For Eeg Recordingsmentioning

confidence: 99%

Autoencoders with deformable convolutions for latent representation of EEG spectrograms in classification tasks

Zubrikhina,

Masnyi,

Hamoudi

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Electroencephalogram (EEG) is a set of time series each of which can be represented as a 2D image (spectrogram), so that EEG recording can be mapped to the C-dimensional image (where C denotes the number of channels in the image and equals to the number of electrodes in EEG montage). In this paper, a novel approach for automated feature extraction from spectrogram representation is proposed. The method involves the usage of autoencoder models based on 3-dimensional convolution layers and 2-dimensional deformable convolution layers. Features, extracted by autoencoders, can be used to classify patients with Major Depressive Disorder (MDD) from healthy controls based on resting-state EEG. The proposed approach outperforms baseline ML models trained on spectral features extracted manually. The source code is available on: source code

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Section: Application Of Autoencoder Based Models For Eeg Recordingsmentioning

confidence: 99%