Deep learning convolutional neural networks have proved to be a powerful tool for MRI analysis. In current work, we explore the potential of the deformable convolutional deep neural network layers for MRI data classification. We propose new 3D deformable convolutions(d-convolutions), implement them in VoxResNet architecture and apply for structural MRI data classification. We show that 3D d-convolutions outperform standard ones and are effective for unprocessed 3D MR images being robust to particular geometrical properties of the data. Firstly proposed dVoxResNet architecture exhibits high potential for the use in MRI data classification.
@article{arxiv.1911.01898,
title = {3D Deformable Convolutions for MRI classification},
author = {Marina Pominova and Ekaterina Kondrateva and Maksim Sharaev and Sergey Pavlov and Alexander Bernstein and Evgeny Burnaev},
journal= {arXiv preprint arXiv:1911.01898},
year = {2019}
}
Comments
Accepted to IEEE International Conference on Machine Learning and Applications (ICMLA 2019)