| --- |
| 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) |
|
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| 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. |
|
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| ## Model Description |
|
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| 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). |
|
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| ### 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 |
|
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| ## Intended Uses |
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| This encoder is particularly suited for: |
| - Transfer learning on T1-weighted MRI tasks |
| - Feature extraction for structural MRI analysis |
| - General brain MRI representation learning |
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|
| [arXiv](https://arxiv.org/abs/2501.12057) |
