Update README.md

README.md

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----
-license: mit
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+---
+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