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tarirozw/
gatekeeper_cervix_detector

Name: gatekeeper_cervix_detector
Availability: InStock

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Gatekeeper Cervix Detector

This model serves as a binary gatekeeper for cervical cancer screening systems. It quickly determines whether an input image is a valid cervical image before passing it to a downstream diagnostic model (MedSigLip).

Model Details

Model Name: Gatekeeper Cervix Detector
Base Architecture: MobileNetV3-Small
Task: Binary classification (cervix vs not-cervix)
Input Size: 224×224×3 (RGB)
Input Normalization: [-1, 1] (mean = [0.5, 0.5, 0.5], std = [0.5, 0.5, 0.5])
Output: Probability that the image contains a cervix (sigmoid output)
Threshold: 0.70 (images below this confidence are rejected)
License: CC BY 4.0
Repository: [Link to this repo]

Intended Use

This model is designed to be used as the first stage in a two-stage pipeline:

Gatekeeper (this model): Filters out non-cervix images (random photos, other medical images, poor quality, etc.).
MedSigLip (downstream model): Only runs if the gatekeeper accepts the image. It performs cancer stage classification or similarity scoring.

Use Case: Maternal health screening in low-resource settings where non-experts may capture images using blind sweeps.

Performance

Test Set Results (threshold = 0.70)

Accuracy: 99.94%
Precision: 100.00%
Recall/Sensitivity: 99.88%
Specificity: 100.00%
AUC: 1.0000
Rejection Rate: ~52.0%

The model shows excellent generalization and very strong rejection of non-cervix images while maintaining high sensitivity on true cervix images.

Class Distribution

Cervix: 48.0%
Not-cervix: 52.0%
The split is stratified across train/validation/test sets.

How to Use

Loading the Model

python

from transformers import AutoModel
model = AutoModel.from_pretrained("your-username/gatekeeper-cervix-detector")

tarirozw/gatekeeper_cervix_detector