---
language: en
tags:
- medical-imaging
- mri
- self-supervised
- 3d
- neuroimaging
license: apache-2.0
library_name: pytorch
datasets:
- custom
---

# SimCLR-MRI Pre-trained Encoder (SeqAug)

This repository contains a pre-trained 3D CNN encoder for MRI analysis. The model was trained using contrastive learning (SimCLR) with Bloch equation simulations to generate multi-contrast views of the same anatomy.

## Model Description

The encoder is a 3D CNN with 5 convolutional blocks (64, 128, 256, 512, 768 channels), outputting 768-dimensional features. This SeqAug variant treats different simulated MRI sequences as strong augmentations during contrastive learning, encouraging sequence-robust representations.

### Training Procedure
- **Pre-training Data**: 51 qMRI datasets (22 healthy, 29 stroke subjects)
- **Augmentations**: Bloch simulation-based sequence augmentation + standard transformations
- **Input**: 3D MRI volumes (96×96×96)
- **Output**: 768-dimensional feature vectors

## Intended Uses

This encoder is particularly suited for:
- Cross-sequence transfer learning
- Multi-contrast MRI analysis
- Sequence-robust feature extraction