---
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