|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- text-classification |
|
|
- setfit |
|
|
- sentence-embedding |
|
|
- eye-imaging |
|
|
- ophthalmology |
|
|
- medical-imaging |
|
|
- fair-data |
|
|
- eyeact |
|
|
--- |
|
|
|
|
|
# Envision Eye Imaging Classifier |
|
|
|
|
|
SetFit few-shot classifier for identifying eye imaging datasets from scientific metadata. |
|
|
|
|
|
**Developed by**: FAIR Data Innovations Hub in collaboration with the EyeACT Study |
|
|
|
|
|
## Model Description |
|
|
|
|
|
Uses `Alibaba-NLP/gte-large-en-v1.5` as backbone with 4-class classification: |
|
|
|
|
|
- **EYE_IMAGING (3)**: Actual ophthalmic imaging datasets (fundus, OCT, OCTA, cornea) |
|
|
- **EYE_SOFTWARE (2)**: Code, tools, models for eye imaging |
|
|
- **EDGE_CASE (1)**: Eye research papers, reviews, non-imaging data |
|
|
- **NEGATIVE (0)**: Not eye-related |
|
|
|
|
|
## Results on Zenodo |
|
|
|
|
|
Tested on 515 Zenodo datasets (filtered to `resource_type=dataset` only): |
|
|
|
|
|
| Class | Count | |
|
|
|-------|-------| |
|
|
| EYE_IMAGING | 120 | |
|
|
| EYE_SOFTWARE | 66 | |
|
|
| EDGE_CASE | 3 | |
|
|
| NEGATIVE | 325 | |
|
|
|
|
|
### Confidence Distribution (EYE_IMAGING) |
|
|
|
|
|
| Confidence | Count | % | |
|
|
|------------|-------|---| |
|
|
| High (≥0.95) | 117 | 97.5% | |
|
|
| Medium (0.80-0.95) | 2 | 1.7% | |
|
|
| Lower (<0.80) | 1 | 0.8% | |
|
|
|
|
|
### Data Pipeline |
|
|
|
|
|
- Scraped with datasets-only filter |
|
|
- ZIP contents inspected via HTTP Range requests (31,958 files catalogued) |
|
|
- Genomics files excluded (.fasta, .h5ad, .vcf, etc.) |
|
|
|
|
|
## Training |
|
|
|
|
|
- **Examples**: 452 (99 positive, 30 software, 90 edge case, 233 negative) |
|
|
- **Epochs**: 2 |
|
|
- **Batch Size**: 16 |
|
|
|
|
|
## Usage |
|
|
|
|
|
```python |
|
|
from sentence_transformers import SentenceTransformer |
|
|
import joblib |
|
|
|
|
|
model = SentenceTransformer("jimnoneill/envision-eye-imaging-classifier", trust_remote_code=True) |
|
|
head = joblib.load("model_head.pkl") |
|
|
|
|
|
embeddings = model.encode(["Retinal OCT dataset for diabetic retinopathy"]) |
|
|
predictions = head.predict(embeddings) |
|
|
``` |
|
|
|
|
|
## Citation |
|
|
|
|
|
- EyeACT Envision project |
|
|
- FAIR Data Innovations Hub (fairdataihub.org) |
|
|
- Alibaba-NLP/gte-large-en-v1.5 |
|
|
|
|
|
## Contact |
|
|
|
|
|
EyeACT team: [eyeactstudy.org](https://eyeactstudy.org) |
|
|
|
|
|
|
