Duplicate from dawahealth/gatekeeper_cervix_detector

Co-authored-by: Khanyisile Tapiwa Magagula <KhanyiTapiwa00@users.noreply.huggingface.co>

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README.md

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+---
+license: mit
+---
+# 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:
+
+1. **Gatekeeper** (this model): Filters out non-cervix images (random photos, other medical images, poor quality, etc.).
+2. **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")

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gatekeeper_metadata.json

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+{
+  "dataset_name": "Gatekeeper Binary Dataset (Preprocessed)",
+  "purpose": "Binary gatekeeper for cervix detection before MedSigLip",
+  "original_image_size": "448x448",
+  "preprocessed_image_size": "224x224",
+  "normalization": "[-1, 1] with mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]",
+  "total_samples": 11547,
+  "classes": {
+    "cervix": {
+      "label": 1,
+      "count": 5543
+    },
+    "not_cervix": {
+      "label": 0,
+      "count": 6004
+    }
+  },
+  "split": {
+    "train": "70%",
+    "validation": "15%",
+    "test": "15%"
+  },
+  "stratified": true,
+  "balance_ratio": "0.923",
+  "model": "MobileNetV3-Small",
+  "threshold": 0.70,
+  "input_shape": [224, 224, 3],
+  "note": "Images have been resized to 224x224 and normalized to [-1, 1]. This file describes the version used for training the gatekeeper model."
+}