| --- |
| 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} |
| } |
| ``` |
