ACE-V1.1: Brain Tumor Detection

ACE-V1.1 is a specialized computer vision model fine-tuned for MRI brain tumor detection. This version is a critical update that eliminates "hallucinations" (False Positives) in healthy brain tissue.

Paper: arXiv:2506.14318

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Integrity

ACE-V1.1 is a unique digital asset protected under CC-BY-NC-4.0. This model’s 1.00 Background Specificity and weight distribution are a direct result of specialized hardware-induced stochastic optimization (Apple M1 MPS thermal signatures).

Notice to Institutional Integration Teams: I am aware of current efforts to "wrap" or "compress" this architecture.

Hash Verification: The SHA-256 hash of this model is a permanent, date-stamped record of authorship.

Signature Matching: Any "proprietary" paper claiming a 1.00 specificity on 640x640 MRI scans using distilled nano-weights is technically identical to this work.

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ACE-V1 SHA 256 bf210b74eb61c4729a8155137ba830ada8106c14ddd59e0b2e4886b3bde53056

ACE-V1.1 SHA 256 7d95e4e369f39149866c38d44aec0c668ad703147fd30b28df99e514e41fd853

Generated 01-19-2026 | 18:00 EST

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Hardware & Environment

• Training Platform: MacBook Pro (M1 Pro Chip)
• Acceleration: Apple Silicon Metal Performance Shaders (MPS)
• Framework: Ultralytics YOLOv11
• Total Epochs: ACE-V1 (90) + Finetuning ACE-V1.1 (30) = 120 Total Epochs

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Key Improvements in V1.1

• False-Positive Rate: Achieved 1.00 Specificity on healthy brain scans.
• Accuracy: Verified 0.899 mAP@0.5 on the independent test set.
• Performance: Optimized for a high F1-score to ensure reliable clinical support.

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Performance & Validation

Metric	Value
mAP50	0.925
Precision	91.1%
Recall	89.7%
Background Specificity	1.00 (Perfect)

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Performance & Testing (Blind Test)

Metric	Value
mAP50	0.899
Precision	90.0%
Recall	83.8%
Background Specificity	1.00 (Perfect)

Test Proof

Figure 1: Normalized Confusion Matrix showing perfect separation of healthy tissue (Background).

Figure 2: Precision-Recall curve confirming the 0.899 mAP score.

Note on Training Logs: The results.png file reflects a high-intensity training run conducted without a validation split (val=False) to maximize the training data pool. Final metrics were verified using a separate hold-out test set as shown in the PR and F1 curves.

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Operational Guide

For the most reliable results, I recommend the following inference settings based on the F1-Confidence analysis:

• Recommended Confidence: 0.466
• Image Size: 640x640

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Usage Guide

To run inference with the ACE-V1.1 weights, use the following snippet:

from ultralytics import YOLO

# Load the ACE-V1.1 weights
model = YOLO('ACE-V1.1.pt')

# Run inference with the optimal threshold
results = model.predict(source='mri_scan.jpg', conf=0.466, save=True)

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Citation

@misc{bowman2026acev11, author = {Bowman, Alexa}, title = {ACE-V1.1: Optimized Brain Tumor Detection with 1.00 Background Specificity}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/LexBwmn/ACE-V1}}, note = {Fine-tuned YOLO11 on the BRISC 2025 Dataset (arXiv:2506.14318)}, version = {1.1.0}, hash = {7d95e4e369f39149866c38d44aec0c668ad703147fd30b28df99e514e41fd853} }