AegisOSS/stage-1

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)

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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.

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Dataset Attribution & Citation

This model was trained on public histopathology research datasets:

• LC25000 (CC-BY-4.0) — Lung & Colon (25,000 patches)
• Patch Camelyon (CC0-1.0) — Lymph node metastases
• Breast Histopathology Patches (CC-BY-4.0) — IDC breast cancer
• Skin Lesion HM10000 (MIT) — Melanoma/benign
• Camelyon17 (jxie/djghosh) — Lymph node WSI patches (2 subsets)

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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.

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Related Models

• Stage 2 OncoTree Subtype Classifier: AegisOSS/stage-2 — 9-class ResNet-50 for cancer subtyping

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Citation

@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}
}