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glaucoma-pipeline
yashvmaurya/glaucoma-pipeline
glaucoma-pipeline is a machine learning model from yashvmaurya. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Artifacts for a two-stage glaucoma decision pipeline.
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Updated Jun 30, 2026
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.pt32.9 MB · 61%
At a glance
- License
- mit
- Access
- Public
- Created
- Jun 30, 2026
- Updated
- Jun 30, 2026
- SHA
- 9b077f90
Model card
From the Hugging Face model README
Glaucoma Two-Stage AI Pipeline
Artifacts for a two-stage glaucoma decision pipeline.
- Stage 1 — Detect: EfficientNet-B0 fine-tuned on SMDG-19 fundus images
(
stage1_detect/stage1_best.pt) with Grad-CAM explainability. External test on REFUGE1. - Stage 2 — Progression (novel core): GRU residual forecaster of the next visual-field
test from history, with calibrated MC-dropout uncertainty
(
stage2_progress/stage2_gru.pt).
Stage 2 verified results (UWHVF, patient-level test set)
- Forecasting: GRU 2.74 dB MAE < last-value 2.78 < pointwise-LR 3.28.
- Calibrated uncertainty: ECE 0.048, 90% interval coverage 91.7%.
- Severity staging: accuracy 0.815, macro-F1 0.743. Fast/slow (MD-slope): AUROC 0.855.
Trained models are released for research/reproducibility. Datasets are public (SMDG-19, REFUGE, UWHVF) and are not redistributed here. Not a medical device.
See the project repo for code, config, and the full reliability report.
- License
- mit
- Created
- Jun 30, 2026
- Updated
- Jun 30, 2026