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 (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