| | --- |
| | 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 |
| |
|
| | [arXiv](https://arxiv.org/abs/2501.12057) |
| |
|
