| | --- |
| | language: en |
| | tags: |
| | - medical-imaging |
| | - mri |
| | - self-supervised |
| | - 3d |
| | - neuroimaging |
| | license: apache-2.0 |
| | library_name: pytorch |
| | datasets: |
| | - custom |
| | --- |
| | |
| | # SimCLR-MRI Pre-trained Encoder (Base) |
| |
|
| | This repository contains a pre-trained 3D CNN encoder for MRI analysis. The model was trained using contrastive learning (SimCLR) on MPRAGE brain MRI scans, using standard image augmentations. |
| |
|
| | ## Model Description |
| |
|
| | The encoder is a 3D CNN with 5 convolutional blocks (64, 128, 256, 512, 768 channels), outputting 768-dimensional features. This base variant was trained on real MPRAGE scans using standard contrastive augmentations (random rotations, flips, intensity changes). |
| |
|
| | ### Training Procedure |
| | - **Pre-training Data**: 51 qMRI datasets (22 healthy, 29 stroke subjects) |
| | - **Augmentations**: Standard geometric and intensity transformations |
| | - **Input**: 3D MPRAGE volumes (96×96×96) |
| | - **Output**: 768-dimensional feature vectors |
| |
|
| | ## Intended Uses |
| |
|
| | This encoder is particularly suited for: |
| | - Transfer learning on T1-weighted MRI tasks |
| | - Feature extraction for structural MRI analysis |
| | - General brain MRI representation learning |
| |
|
| | [arXiv](https://arxiv.org/abs/2501.12057) |
