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

# SimCLR-MRI Pre-trained Encoder (SeqInv)

This repository contains a pre-trained 3D CNN encoder for MRI analysis. The model was trained using contrastive learning (SimCLR) with explicit sequence invariance enforced through paired multi-contrast images.

## Model Description

The encoder is a 3D CNN with 5 convolutional blocks (64, 128, 256, 512, 768 channels), outputting 768-dimensional features. This SeqInv variant was trained on paired sequences generated through Bloch simulations, explicitly enforcing sequence invariance in the learned representations.

### Training Procedure
- **Pre-training Data**: 51 qMRI datasets (22 healthy, 29 stroke subjects)
- **Training Strategy**: Paired sequence views + standard augmentations
- **Input**: 3D MRI volumes (96×96×96)
- **Output**: 768-dimensional sequence-invariant feature vectors

## Intended Uses

This encoder is particularly suited for:
- Sequence-agnostic analysis tasks
- Multi-sequence registration
- Cross-sequence synthesis
- Tasks requiring sequence-invariant features