Add arXiv:2604.12152 citation, full author list, and github.com/sebasmos/latent-sr link

README.md

@@ -1,3 +1,94 @@
 ---
 license: mit
+language:
+- en
+tags:
+- medical-imaging
+- super-resolution
+- embeddings
+- MRI
+- chest-x-ray
+- latent-diffusion
+- VAE-embeddings
+task_categories:
+- image-to-image
 ---
+
+# Latent-SR Embeddings: Precomputed VAE Latents for Medical Image Super-Resolution
+
+Precomputed VAE latent embeddings from the paper:
+
+> **"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution"**  
+> Sebastian Cajas, Ashaba Judith, Rahul Gorijavolu, Sahil Kapadia, Hillary Clinton Kasimbazi, Leo Kinyera, Emmanuel Paul Kwesiga, Sri Sri Jaithra Varma Manthena, Luis Filipe Nakayama, Ninsiima Doreen, Leo Anthony Celi.  
+> arXiv:2604.12152 (2026) — under review at Nature Scientific Reports.
+>
+> 📄 Paper: https://arxiv.org/abs/2604.12152  
+> 💻 Code: https://github.com/sebasmos/latent-sr
+
+## Dataset Description
+
+Each directory contains precomputed encoder outputs (posterior mean, deterministic) for a given (VAE, dataset) pair. These are the latent inputs to the diffusion UNet — sharing them avoids re-encoding during training/evaluation.
+
+Each `.npy` file is a single 2D latent of shape `(C, H, W)`, stored as float32.
+
+## Directory Structure
+
+```
+<vae>_<dataset>/
+├── train_latent/   # training split latents
+├── valid_latent/   # validation split latents
+└── test_latent/    # test split latents
+```
+
+## VAE × Dataset Index
+
+| Directory | VAE | Latent shape | Dataset | Split sizes |
+|-----------|-----|-------------|---------|-------------|
+| `medvae-4-3_brats` | MedVAE (3×64×64) | (3,64,64) | BraTS 2023 brain MRI | train/val/test |
+| `medvae-4-3_cxr`   | MedVAE (3×64×64) | (3,64,64) | MIMIC-CXR chest X-ray | train/val/test |
+| `medvae-4-3_mrnet` | MedVAE (3×64×64) | (3,64,64) | MRNet knee MRI | train/val/test |
+| `sdvae_brats`      | SD-VAE (4×32×32) | (4,32,32) | BraTS 2023 | train/val/test |
+| `sdvae_cxr`        | SD-VAE (4×32×32) | (4,32,32) | MIMIC-CXR | train/val/test |
+| `sdvae_mrnet`      | SD-VAE (4×32×32) | (4,32,32) | MRNet | train/val/test |
+| `medvae-4-1_brats` | MedVAE 4_1 (1×64×64) | (1,64,64) | BraTS | train/val/test |
+| `medvae-4-1_cxr`   | MedVAE 4_1 (1×64×64) | (1,64,64) | MIMIC-CXR | train/val/test |
+| `medvae-4-1_mrnet` | MedVAE 4_1 (1×64×64) | (1,64,64) | MRNet | train/val/test |
+| `medvae-8-1_brats` | MedVAE 8_1 (1×32×32) | (1,32,32) | BraTS | train/val/test |
+| `medvae-8-1_cxr`   | MedVAE 8_1 (1×32×32) | (1,32,32) | MIMIC-CXR | train/val/test |
+| `medvae-8-1_mrnet` | MedVAE 8_1 (1×32×32) | (1,32,32) | MRNet | train/val/test |
+| `medvae-8-4_brats` | MedVAE 8_4 (4×32×32) | (4,32,32) | BraTS | train/val/test |
+| `medvae-8-4_cxr`   | MedVAE 8_4 (4×32×32) | (4,32,32) | MIMIC-CXR | train/val/test |
+| `medvae-8-4_mrnet` | MedVAE 8_4 (4×32×32) | (4,32,32) | MRNet | train/val/test |
+| `medvae-4-4_brats` | MedVAE 4_4 (4×64×64) | (4,64,64) | BraTS | train/val/test |
+| `medvae-4-4_cxr`   | MedVAE 4_4 (4×64×64) | (4,64,64) | MIMIC-CXR | train/val/test |
+| `medvae-4-4_mrnet` | MedVAE 4_4 (4×64×64) | (4,64,64) | MRNet | train/val/test |
+
+## Usage
+
+```python
+import numpy as np
+from huggingface_hub import snapshot_download
+
+# Download all embeddings for MedVAE on BraTS
+local_dir = snapshot_download("sebasmos/latent-sr-embeddings")
+latents = np.load(f"{local_dir}/medvae-4-3_brats/valid_latent/000001.npy")
+print(latents.shape)  # (3, 64, 64)
+```
+
+## Citation
+
+```bibtex
+@article{cajas2026domain,
+  title   = {Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution},
+  author  = {{Sebastian Cajas} and {Ashaba Judith} and {Rahul Gorijavolu} and {Sahil Kapadia} and {Hillary Clinton Kasimbazi} and {Leo Kinyera} and {Emmanuel Paul Kwesiga} and {Sri Sri Jaithra Varma Manthena} and {Luis Filipe Nakayama} and {Ninsiima Doreen} and {Leo Anthony Celi}},
+  journal = {arXiv preprint arXiv:2604.12152},
+  year    = {2026},
+  url     = {https://arxiv.org/abs/2604.12152}
+}
+```
+
+Code: https://github.com/sebasmos/latent-sr · Paper: https://arxiv.org/abs/2604.12152
+
+## License
+
+MIT License.