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
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
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