---
language:
- en
license: cc-by-4.0
tags:
- medical
- histopathology
- cancer-detection
- binary-classification
- efficientnet
- pytorch
- onnx
- int8
metrics:
- accuracy
- recall
- f1
- auc
library_name: timm
pipeline_tag: image-classification
model-index:
- name: Aegis Stage 1 Binary Cancer Detector
  results:
  - task:
      type: image-classification
      name: Histopathology Cancer vs Benign Detection
    metrics:
    - name: Test Accuracy
      type: accuracy
      value: 0.9209
    - name: Test Recall (Sensitivity)
      type: recall
      value: 0.9456
    - name: Test Precision
      type: precision
      value: 0.8821
    - name: Test F1-Score
      type: f1
      value: 0.9127
    - name: ROC-AUC
      type: auc
      value: 0.9814
---

# Aegis Stage 1 — Binary Cancer Detector (EfficientNet-B0)

**Aegis Stage 1** is a lightweight, high-sensitivity binary classifier trained on **30,453 histopathology tiles** across 6 public datasets to distinguish **cancerous** from **non-cancerous** tissue. It serves as the first stage in the Aegis cascaded pipeline for automated cancer detection and OncoTree subtype classification.

- **Architecture**: EfficientNet-B0 (`timm`)
- **Total Parameters**: 4,008,829 (~4.01 Million)
- **INT8 ONNX Model Size**: **15.7 MB**
- **Test Accuracy**: **92.09%**
- **Test Recall (Cancer Sensitivity)**: **94.56%**
- **Test F1-Score**: **91.27%**
- **ROC-AUC**: **0.9814**
- **License**: [Creative Commons Attribution 4.0 (CC-BY-4.0)](https://creativecommons.org/licenses/by/4.0/)

---

## Performance Benchmarks (Unseen Test Set)

Evaluated across **3,528 unseen histopathology test tiles** (6 datasets):

| Dataset | Accuracy | Recall | Precision | F1-Score | ROC-AUC | Missed Cancer Rate |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|
| **LC25000** | 99.34% | 98.92% | 100.00% | 99.46% | 0.9979 | 1.08% |
| **Camelyon17 (djghosh)** | 98.75% | 98.22% | 99.28% | 98.75% | 0.9987 | 1.78% |
| **Camelyon17 (jxie)** | 96.12% | 96.17% | 93.39% | 94.76% | 0.9960 | 3.83% |
| **Patch Camelyon** | 88.83% | 93.59% | 85.39% | 89.30% | 0.9656 | 6.41% |
| **Breast Histopathology** | 87.08% | 82.01% | 76.73% | 79.28% | 0.9371 | 17.99% |
| **Skin Lesion HM10000** | 81.03% | 92.55% | 67.18% | 77.85% | 0.9201 | 7.45% |
| **Overall (Weighted)** | **92.09%** | **94.56%** | **88.21%** | **91.27%** | **0.9814** | **5.44%** |

### High-Recall Operating Mode (Clinical Screening)

At threshold `0.32`, the model achieves **95.07% cancer recall** with **89.32% specificity** — ideal for triage/screening workflows where missed cancers must be minimized.

---

## Dataset Attribution & Citation

This model was trained on public histopathology research datasets:

- **LC25000** ([CC-BY-4.0](https://huggingface.co/datasets/1aurent/LC25000)) — Lung & Colon (25,000 patches)
- **Patch Camelyon** ([CC0-1.0](https://huggingface.co/datasets/1aurent/Patch-Camelyon)) — Lymph node metastases
- **Breast Histopathology Patches** ([CC-BY-4.0](https://huggingface.co/datasets/dbzadnen/breast-histopathology-images)) — IDC breast cancer
- **Skin Lesion HM10000** ([MIT](https://huggingface.co/datasets/ltl1313/Skin-lesion-hm10000-binary)) — Melanoma/benign
- **Camelyon17 (jxie/djghosh)** — Lymph node WSI patches (2 subsets)

---

## Medical Research Disclaimer

**Aegis is an open-source AI research prototype intended for educational, scientific evaluation, and research purposes only.** It is not a certified medical device and must not be used for primary clinical diagnosis or treatment planning.

---

## Related Models

- **Stage 2 OncoTree Subtype Classifier**: [`AegisOSS/stage-2`](https://huggingface.co/AegisOSS/stage-2) — 9-class ResNet-50 for cancer subtyping

---

##  Citation

```bibtex
@software{aegis_stage1,
  title = {Aegis Stage 1: EfficientNet-B0 Binary Cancer Detector for Histopathology},
  author = {Ranveer Soni},
  year = {2026},
  url = {https://huggingface.co/AegisOSS/stage-1},
  license = {CC-BY-4.0}
}
```