EyeeSEE
Repository: Nj-1111/EyeeSEE Dataset: Nj-1111/GlaucomaDataset Demo: Nj-1111/EyeeSEE (Spaces)
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 355 MB
- Versión
- vRevision 0564502
- Actualizado
- 9 oct 2026
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
EyeeSEE — Glaucoma Segmentation Model
Repository: Nj-1111/EyeeSEE
Dataset: Nj-1111/Glaucoma_Dataset
Demo: Nj-1111/EyeeSEE (Spaces)
Model Description
EyeeSEE is a U-Net segmentation model trained to delineate the Optic Disc and Optic Cup in retinal fundus photographs for automated glaucoma screening. It is the core inference engine of a Clinical Decision Support System (CDSS) that computes deterministic clinical metrics — vCDR, ISNT rim thickness, and three-tier risk stratification — from predicted segmentation masks.
Monte Carlo Dropout is used at inference time to produce a per-prediction uncertainty score, flagging low-confidence cases for clinical review.
Model Architecture
| Component | Detail |
|---|---|
| Architecture | U-Net with skip connections |
| Input | Grayscale fundus image, 512 × 512 |
| Output | 3-class segmentation map (Background / Optic Disc / Optic Cup) |
| Base filters | 64 |
| Dropout | Dropout2d, rate = 0.1 |
| Parameters | ~31M |
| Uncertainty | Monte Carlo Dropout (N passes at inference) |
Training
| Detail | Value |
|---|---|
| Dataset | Nj-1111/Glaucoma_Dataset |
| Training images | ~13,300 |
| Validation images | ~770 |
| Optimizer | Adam, lr = 5e-5 |
| Loss | 0.5 × Dice + 0.5 × Weighted CrossEntropy |
| Class weights | Background 0.1 · Disc 1.5 · Cup 3.0 |
| Hardware | Kaggle GPU (T4) |
| Framework | PyTorch 2.x |
Dataset
Curated from three public sources with a unified preprocessing pipeline:
| Source | Description |
|---|---|
| EyePACS | Large-scale diabetic retinopathy screening dataset |
| SMDG | Structured multi-disease glaucoma dataset |
| REFUGE2 | Retinal Fundus Glaucoma Challenge (segmentation reference) |
Split:
| Split | Images |
|---|---|
| Train | ~13,300 |
| Validation | ~770 |
| Test | ~770 |
Each image is labelled as NRG (Non-Referable Glaucoma / Normal) or RG (Referable Glaucoma).
Clinical Metrics (Phase 3 Pipeline)
After segmentation, the following are computed deterministically:
vCDR — Vertical Cup-to-Disc Ratio
Vertical diameter of cup ÷ vertical diameter of disc. Primary glaucoma screening metric.
| vCDR | Clinical Interpretation |
|---|---|
| < 0.65 | Healthy |
| 0.65 – 0.80 | Glaucoma Suspect |
| > 0.80 | High Risk |
ISNT Rule
Neuro-retinal rim thickness measured in four quadrants. Healthy eyes satisfy: Inferior > Superior > Nasal > Temporal. Violation is an early indicator of glaucomatous damage.
Risk Stratification
Three-tier output: Healthy · Glaucoma Suspect · High Risk.
Checkpoints
Checkpoints are saved to checkpoints/ during training and named:
checkpoints/checkpoint_epoch_NNN.pt
Each checkpoint contains model weights, optimiser state, scheduler state, validation metrics, and best validation loss.
To load the latest checkpoint for inference:
from checkpoint_loader import load_model_for_inference
from model import UNet
model = UNet(in_channels=1, n_classes=3, base_filters=64, dropout=0.3)
model = load_model_for_inference(
model=model,
repo_id="Nj-1111/EyeeSEE",
epoch=None, # None = latest
token="hf_..."
)
Inference
Via HuggingFace Spaces UI
Visit huggingface.co/spaces/Nj-1111/EyeeSEE, upload a fundus image (JPEG or PNG), and click Analyse.
Via Python Client
from gradio_client import Client, handle_file
client = Client("Nj-1111/EyeeSEE")
result = client.predict(
file_path=handle_file("fundus.jpg"),
api_name="/analyse"
)
text_report = result[0]
pdf_path = result[1]
print(text_report)
Via REST API
# JSON report
curl -X POST https://nj-1111-eyeesee.hf.space/predict \
-F "[email protected]"
# PDF report
curl -X POST https://nj-1111-eyeesee.hf.space/predict/pdf \
-F "[email protected]" --output report.pdf
Programmatic (Python)
from phase3pipeline import Phase3Pipeline
pipeline = Phase3Pipeline(
repo_id="Nj-1111/EyeeSEE",
epoch=None,
mc_passes=10,
token="hf_..."
)
import cv2
image = cv2.imread("fundus.jpg")
result = pipeline.run(image)
print(result["report"]["vcdr"])
print(result["report"]["risk_level"])
Limitations
- Segmentation masks are trained on synthetic circular approximations of the disc and cup. Performance on pathological cases may be reduced until real ground-truth annotations are incorporated.
- vCDR thresholds are population-level estimates and do not account for disc size, ethnicity, or myopia.
- Eye laterality (nasal/temporal swap between left and right eyes) is not currently handled. ISNT values should be interpreted directionally with caution.
- This model has not been validated in a clinical trial. It is a research prototype.
Intended Use
| Use | Status |
|---|---|
| Research and academic study | ✓ Intended |
| Preliminary screening assistance | ✓ Intended with supervision |
| Clinical diagnosis without ophthalmologist review | ✗ Not intended |
| Standalone medical device | ✗ Not intended |
Disclaimer
This model is a research prototype and is NOT a certified medical device.
All outputs must be validated by a qualified ophthalmologist before any clinical action is taken.
The authors accept no liability for clinical decisions made on the basis of this model's output.
Citation
If you use this model or dataset in your research, please cite:
@misc{eyeesee2025,
author = {Nj-1111},
title = {EyeeSEE: Glaucoma Clinical Decision Support System},
year = {2025},
publisher = {HuggingFace},
url = {https://huggingface.co/Nj-1111/EyeeSEE}
}
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 25 ago
Artefactos del modelo
51 artefactos
Artefactos del modelo
51 artefactosExtractos de fuentes
2 extractos
Extractos de fuentes
2 extractos--- license: openrail datasets: - Nj-1111/Glaucoma_Dataset pipeline_tag: image-segmentation --- # EyeeSEE — Glaucoma Segmentation Model **Repository:** `Nj-1111/EyeeSEE` **Dataset:** [`Nj-1111/Glaucoma_Dataset`](https://huggingface.co/datasets/Nj-1111/Glaucoma_Dataset) **Demo:** [`Nj-1111/EyeeSEE` (Spaces)](https://huggingface.co/spaces/Nj-1111/EyeeSEE) --- ## Model Description EyeeSEE is a U-Net segmentation model trained to delineate the **Optic Disc** and **Optic Cup** in retinal fundus photographs for automated glaucoma screening. It is the core inference engine of a Clinical Decision Support System (CDSS) that computes deterministic clinical metrics — vCDR, ISNT rim thickness, and three-tier risk stratification — from predicted segmentation masks. Monte Carlo Dropout is used at inference time to produce a per-prediction uncertainty score, flagging low-confidence cases for clinical review. --- ## Model Architecture | Component | Detail | |---|---| | Architecture | U-Net with skip connections | | Input | Grayscale fundus image, 512 × 512 | | Output | 3-class segmentation map (Background / Optic Disc / Optic Cup) | | Base filters | 64 | | Dropout | Dropout2d, rate = 0.1 | | Parameters | ~31M | | Uncertainty | Monte Carlo Dropout (N passes at inference) | --- ## Training | Detail | Value | |---|---| | Dataset | Nj-1111/Glaucoma_Dataset | | Training images | ~13,300 | | Validation images | ~770 | | Optimizer | Adam, lr = 5e-5 | | Loss | 0.5 × Dice + 0.5 × Weighted CrossEntropy | | Class weights | Background 0.1 · Disc 1.5 · Cup 3.0 | | Hardware | Kaggle GPU (T4) | | Framework | PyTorch 2.x | --- ## Dataset **[`Nj-1111/Glaucoma_Dataset`](https://huggingface.co/datasets/Nj-1111/Glaucoma_Dataset)** Curated from three public sources with a unified preprocessing pipeline: | Source | Description | |---|---| | EyePACS | Large-scale diabetic retinopathy screening dataset | | SMDG | Structured multi-disease glaucoma dataset | | REFUGE2 | Retinal Fundus Glaucoma Challenge (segmentation reference) | **Split:** | Split | Images | |---|---| | Train | ~13,300 | | Validation | ~770 | | Test | ~770 | Each image is labelled as **NRG** (Non-Referable Glaucoma / Normal) or **RG** (Referable Glaucoma). --- ## Clinical Metrics (Phase 3 Pipeline) After segmentation, the following are computed deterministically: **vCDR — Vertica...
Source context: 0 downloads · 2 likes · Pipeline image-segmentation · Repo Nj-1111/EyeeSEE
