Update README.md

README.md

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 ---
 license: cc-by-nc-nd-4.0
+task_categories:
+  - image-to-image
+language:
+  - en
+tags:
+  - medical
+  - ophthalmology
+  - glaucoma
+  - OCT
+  - fundus
+  - RNFLT
+  - deep-learning
+  - tensorflow
+  - keras
+pretty_name: Fundus2RNFLTNorm
+viewer: false
 ---
+
+# Dataset Card: Fundus2RNFLTNorm Weights
+
+## Dataset Summary
+
+Pretrained model weights for **Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma** (Yildiz et al., 2026).
+
+Fundus2RNFLTNorm is a deep learning framework that predicts a personalized expected-normal retinal nerve fiber layer thickness (RNFLT) map from an OCT en face fundus image. The predicted RNFLT norm is intended to capture patient-specific retinal anatomy and can be compared with the observed RNFLT map to derive a personalized RNFLT deviation map for glaucoma research.
+
+This repository provides the pretrained weights associated with the Fundus2RNFLTNorm project.
+
+**Code:** https://github.com/Harvard-AI-and-Robotics-Lab/Fundus2RNFLTNorm
+
+**Paper:** https://doi.org/10.64898/2026.05.26.26354081
+
+## Dataset Details
+
+### Dataset Description
+
+| Field | Value |
+|---|---|
+| Institution | Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School |
+| Task | Personalized RNFLT normative map prediction |
+| Input | OCT en face fundus image |
+| Output | Predicted personalized expected-normal RNFLT map |
+| Architecture | U-Net-based deep learning model |
+| Framework | TensorFlow / Keras |
+| Input resolution | 256 × 256 |
+| Model weights | `octfundus_to_rnflt_model.h5` |
+| License | CC BY-NC-ND 4.0 |
+
+- **Authors:** Elif Yildiz, Lucy Zha, Nazlee Zebardast, Min Shi, Mengyu Wang
+- **Contact:** harvardophai@gmail.com, harvardairobotics@gmail.com
+
+### Model File
+
+| File | Description |
+|---|---|
+| `octfundus_to_rnflt_model.h5` | Pretrained OCT en face fundus → personalized RNFLT norm prediction model |
+
+## How to Download
+
+### Download from the Hugging Face website
+
+Open **Files and versions** and download:
+
+```text
+octfundus_to_rnflt_model.h5
+```
+
+Place the downloaded file in the `checkpoint/` directory of the Fundus2RNFLTNorm GitHub repository:
+
+```text
+Fundus2RNFLTNorm/
+├── checkpoint/
+│   └── octfundus_to_rnflt_model.h5
+├── models/
+├── samples/
+├── utils/
+├── infer.py
+├── inference.ipynb
+├── octfundus2rnflt.py
+├── requirements.txt
+└── train.py
+```
+
+### Download with Hugging Face Hub
+
+```bash
+pip install -U huggingface_hub
+
+hf download harvardairobotics/Fundus2RNFLTNorm \
+  octfundus_to_rnflt_model.h5 \
+  --repo-type dataset \
+  --local-dir checkpoint
+```
+
+## Usage
+
+The released implementation uses:
+
+- Python 3.8
+- TensorFlow 2.4.0
+- OpenCV-Python 4.5.5
+
+Clone the code repository:
+
+```bash
+git clone https://github.com/Harvard-AI-and-Robotics-Lab/Fundus2RNFLTNorm.git
+cd Fundus2RNFLTNorm
+```
+
+Load a sample OCT en face fundus image:
+
+```python
+from octfundus2rnflt import *
+import cv2
+import numpy as np
+
+oct_fundus = cv2.resize(
+    cv2.imread("samples/sample_oct_fundus.png", 0),
+    (256, 256)
+)
+
+img = np.array([
+    np.transpose(
+        np.array([oct_fundus, oct_fundus, oct_fundus]),
+        (1, 2, 0)
+    )
+])
+
+mask = np.ones_like(img)
+```
+
+Load the pretrained model:
+
+```python
+octfundus2rnflt = PCModel(img_rows=256, img_cols=256)
+
+octfundus2rnflt.load(
+    "checkpoint/octfundus_to_rnflt_model.h5",
+    train_bn=False,
+    lr=0.00001
+)
+```
+
+Generate the predicted personalized RNFLT norm map:
+
+```python
+pred = octfundus2rnflt.model.predict([img, mask])[0][:, :, 0]
+
+plot_2dmap(pred, show_cup=True)
+```
+
+A complete example is provided in `inference.ipynb` in the GitHub repository.
+
+> **Compatibility note:** Earlier versions of the GitHub code refer to the checkpoint as `octfundus_to_rnflt_new2.final.h5`. If using such a version, either update the checkpoint path in the code to `octfundus_to_rnflt_model.h5` or rename the downloaded file accordingly.
+
+## Study Overview
+
+The model was developed using OCT scans and visual fields from the Massachusetts Eye and Ear Glaucoma Service. In the associated study, 10,000 OCT scans with normal visual fields were used for model training, and 8,000 additional OCT scans were used for evaluation.
+
+The model predicts patient-specific expected-normal RNFLT maps from OCT en face fundus images. These predictions can be compared with observed RNFLT maps to generate personalized RNFLT deviation maps and support research into glaucoma structure-function relationships.
+
+## Uses
+
+### Direct Use
+
+The pretrained weights are intended for research applications including:
+
+- Reproducing the Fundus2RNFLTNorm model without retraining
+- Predicting personalized expected-normal RNFLT maps from OCT en face fundus images
+- Generating personalized RNFLT deviation maps
+- Investigating glaucoma structure-function relationships
+- Developing and evaluating personalized OCT normative modeling methods
+
+### Out-of-Scope Use
+
+These weights are provided for research purposes only.
+
+They are not intended for autonomous clinical diagnosis, clinical decision-making, treatment selection, or direct patient care. The associated study was released as a preprint and should be interpreted in that context.
+
+## Training Data
+
+The underlying clinical data are private, internal data owned by Massachusetts Eye and Ear and are not distributed through this repository.
+
+The associated study used:
+
+- 10,000 OCT scans with normal visual fields for training
+- 8,000 OCT scans for evaluation, including 2,419 scans with normal visual fields
+
+See the associated publication for complete cohort definitions and evaluation methodology.
+
+## Access
+
+The weights are released under the **CC BY-NC-ND 4.0** license for non-commercial research use.
+
+The “Harvard” designation indicates that this research originated from investigators affiliated with the Department of Ophthalmology at Harvard Medical School and Massachusetts Eye and Ear. It does not imply endorsement, sponsorship, or assumption of responsibility by Harvard University or Harvard Medical School.
+
+## Citation
+
+If you use the Fundus2RNFLTNorm code or pretrained weights, please cite:
+
+```bibtex
+@article{yildiz2026rnfltnorm,
+  title={Deep Learning Prediction of Personalized Peripapillary Retinal Nerve Fiber Layer Thickness Norms from Fundus Images in Glaucoma},
+  author={Yildiz, Elif and Zha, Lucy and Zebardast, Nazlee and Shi, Min and Wang, Mengyu},
+  journal={medRxiv},
+  year={2026},
+  doi={10.64898/2026.05.26.26354081},
+  url={https://pubmed.ncbi.nlm.nih.gov/42245038/}
+}
+```
+
+## Contact
+
+For questions about the pretrained model or code, please contact:
+
+- harvardophai@gmail.com
+- harvardairobotics@gmail.com