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 (SeqAug)
+
+This repository contains a pre-trained 3D CNN encoder for MRI analysis. The model was trained using contrastive learning (SimCLR) with Bloch equation simulations to generate multi-contrast views of the same anatomy.
+
+## Model Description
+
+The encoder is a 3D CNN with 5 convolutional blocks (64, 128, 256, 512, 768 channels), outputting 768-dimensional features. This SeqAug variant treats different simulated MRI sequences as strong augmentations during contrastive learning, encouraging sequence-robust representations.
+
+### Training Procedure
+- **Pre-training Data**: 51 qMRI datasets (22 healthy, 29 stroke subjects)
+- **Augmentations**: Bloch simulation-based sequence augmentation + standard transformations
+- **Input**: 3D MRI volumes (96×96×96)
+- **Output**: 768-dimensional feature vectors
+
+## Intended Uses
+
+This encoder is particularly suited for:
+- Cross-sequence transfer learning
+- Multi-contrast MRI analysis
+- Sequence-robust feature extraction