metadata

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