Aegis Stage 2 is a high-performance deep learning model trained on 10,986 real histopathology tiles across 8 mapped data sources to perform fine-grained cancer subtype categorization model.

Architecture: ResNet-50 (timm)
Total Parameters: 23,526,473 (~23.53 Million)
INT8 ONNX Model Size: 22.66 MB
Test Accuracy: 99.77% (425 / 426 test tiles correct)
Macro-F1 Score: 99.67%
License: Creative Commons Attribution 4.0 (CC-BY-4.0)

Performance Benchmarks (Unseen Test Set)

Evaluated across 426 unseen histopathology test tiles:

Subtype	Class Name	Support	Precision	Recall	F1-Score	Status
BRCA	Invasive Breast Carcinoma	171	1.0000	1.0000	1.0000	🎯 Perfect
GB	Glioblastoma	68	1.0000	1.0000	1.0000	🎯 Perfect
CRC	Colorectal Carcinoma	65	1.0000	1.0000	1.0000	🎯 Perfect
LUAD	Lung Adenocarcinoma	60	1.0000	0.9833	0.9916	⚡ Almost Perfect
LUSC	Lung Squamous Cell	62	0.9841	1.0000	0.9920	⚡ Almost Perfect

Dataset Attribution & Citation

This model was trained on public histopathology research datasets:

LC25000 (CC-BY-4.0)
Dorsar Lung Cancer (MIT License)
Breast Histopathology IDC (CC-BY-4.0)
Brain Tumor Pathology (CC-BY-4.0)

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.

Citation

bibtex

@software{aegis_stage1,
  title = {Aegis Stage 2: OncoTree Subtype Classifier ResNet-50},
  author = {Ranveer Soni},
  year = {2026},
  url = {https://huggingface.co/AegisOSS/stage-2},
  license = {CC-BY-4.0}
}