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 (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