Image Classification
timm
ONNX
PyTorch
English
medical
histopathology
cancer-classification
oncotree
resnet50
int8
Eval Results (legacy)
Instructions to use AegisOSS/stage-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use AegisOSS/stage-2 with timm:
import timm model = timm.create_model("hf_hub:AegisOSS/stage-2", pretrained=True) - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
license: cc-by-4.0
tags:
- medical
- histopathology
- cancer-classification
- oncotree
- resnet50
- pytorch
- onnx
- int8
metrics:
- accuracy
- f1
library_name: timm
pipeline_tag: image-classification
model-index:
- name: Aegis Stage 2 OncoTree Subtype Classifier
results:
- task:
type: image-classification
name: Histopathology Cancer Subtype Classification
metrics:
- name: Test Accuracy
type: accuracy
value: 0.9977
- name: Macro F1-Score
type: f1
value: 0.9967
Aegis Stage 2 — OncoTree Subtype Classifier (ResNet-50)
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 according to the MSK OncoTree Taxonomy.
- 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 | |
| GB | Glioblastoma | 68 | 1.0000 | 1.0000 | 1.0000 | |
| CRC | Colorectal Carcinoma | 65 | 1.0000 | 1.0000 | 1.0000 | |
| LUAD | Lung Adenocarcinoma | 60 | 1.0000 | 0.9833 | 0.9916 | |
| LUSC | Lung Squamous Cell | 62 | 0.9841 | 1.0000 | 0.9920 |
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
@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}
}