Papers › Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the...
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brain
Adrián Arnaiz-Rodríguez, Baris Kanber
The analysis of brain magnetic resonance imaging (MRI) is critical for a proper diagnosis and treatment of neurological diseases. Improvements in this eld can lead to better health quality. Numerous branches can be still enhanced due to the nature of MRI recompilation: disease detection and segmentation, data augmentation, improvement in data collection, or image enhancement are some of them. For several years, many approaches have been taken to address this. Machine Learning and Deep Learning emerge as very popular approaches to solve problems. Several kinds of data mining solutions (supervised, unsupervised, dimension reduction, generative models, etc) and algorithms can be applied to the problem-solving of MRI. Besides, new emerging deep learning architectures for other kinds of image processing tasks can be helpful. New types of convolution, autoencoders or generative adversarial networks are some of them. Therefore, the purpose of this work is to apply one of these new techniques to T1 weighted brain MRI (T1WMRI). We will develop a Deep Convolutional Autoencoder, which can be used to help with some problems in neuroimaging. The input of the Autoencoder will be control T1WMRI and will aim to return the same image, with the problem that, inside its architecture, the image travels through a lower-dimensional space, so the reconstruction of the original image becomes more difficult. Thus, the Autoencoder represents a normative model. This normative model will define a distribution (or normal range) for the neuroanatomical variability for the illness absence. Once trained with these control images, we will discuss the potential application of the autoencoder like noise reducer or disease detector.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| MRI Reconstruction | IXI | Residual U-NET | DSSIM | 1.44e-03 | #1 of 1 | Archive leaderboard | report |
| MRI Reconstruction | IXI | Residual U-NET | MSE | 3.44e-05 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections