# ACE Models: Optimized Brain Tumor Detection
The ACE (Automated Clinical Evaluation) models were developed to solve a critical radiology bottleneck: the widening gap between high-volume imaging demand and the time available for expert human review.
I built these models to prove that high-precision medical computer vision doesn't require massive server infrastructure. My goal was to create efficient diagnostic support that runs locally on a standard MacBook Pro. By prioritizing 1.00 Background Specificity, I designed these models to eliminate the "hallucination" of anomalies in healthy tissue, ensuring reliable performance without the usual computational overhead.
View these models on Hugging Face: https://huggingface.co/LexBwmn/ACE-V1
- ACE-V1: This is the foundational base model. It is available for download within the
archivefile located on my Hugging Face profile. - ACE-V1.1: This is the current, primary iteration. It features refined architecture and enhanced metrics for optimal performance.
MEDICAL RESEARCH USE ONLY. ACE models are NOT a cleared medical device. They must not be used for primary diagnosis or clinical decision-making. All outputs must be verified by a qualified professional.
| Metric | Value |
|---|---|
| mAP50 | 0.899 |
| Precision | 90.0% |
| Recall | 83.8% |
| Background Specificity | 1.00 |
- Mean Pixel Dice (2D): 0.6810
- Mean Pixel Precision: 0.6645
- Mean Pixel Recall: 0.7879
- Inference Speed: 3.95 it/s
- Training Platform: MacBook Pro (M1 Pro)
- Acceleration: Apple Silicon Metal Performance Shaders (MPS)
- Framework: Ultralytics YOLOv11
- Training Strategy: 120 Total Epochs
Due to the custom channel architecture required to maintain 1.00 specificity, these models are optimized for the following settings:
- Confidence Threshold: 0.466
- Input Image Size: 640x640
These models are protected under CC BY-NC-ND 4.0. For institutional adoption, commercial licensing, or clinical integration support, please contact the maintainer.
If you utilize these models in your research, please cite:
@misc{bowman2026ace,
author = {Bowman, Alexa},
title = {ACE Models: Optimized Brain Tumor Detection with 1.00 Background Specificity},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{[https://huggingface.co/LexBwmn/ACE-V1](https://huggingface.co/LexBwmn/ACE-V1)}}
}