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
