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--- |
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model_name: ACE-V1.1 |
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pipeline_tag: object-detection |
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license: cc-by-nc-4.0 |
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links-to-paper: https://arxiv.org/abs/2506.14318 |
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tags: |
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- medical |
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- tumor-detection |
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- yolo |
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- yolo11 |
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- brain tumor |
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- computer vision |
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- ultralytics |
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- arxiv:2506.14318 |
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model-index: |
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- name: ACE-V1.1 |
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results: |
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- task: |
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type: object-detection |
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name: Brain Tumor Detection |
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dataset: |
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name: BRISC 2025 (Fateh et al.) |
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type: external |
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args: |
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kaggle: https://www.kaggle.com/datasets/briscdataset/brisc2025/data |
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metrics: |
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- type: map |
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value: 0.899 |
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name: mAP@0.5 |
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- type: specificity |
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value: 1.00 |
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name: Background Specificity |
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--- |
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# ACE-V1.1: Brain Tumor Detection |
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**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. |
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Paper: [arXiv:2506.14318](https://huggingface.co/papers/2506.14318) |
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--- |
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# Integrity |
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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). |
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Notice to Institutional Integration Teams: |
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I am aware of current efforts to "wrap" or "compress" this architecture. |
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Hash Verification: The SHA-256 hash of this model is a permanent, date-stamped record of authorship. |
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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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--- |
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**ACE-V1 SHA 256** bf210b74eb61c4729a8155137ba830ada8106c14ddd59e0b2e4886b3bde53056 |
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**ACE-V1.1 SHA 256** 7d95e4e369f39149866c38d44aec0c668ad703147fd30b28df99e514e41fd853 |
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Generated 01-19-2026 | 18:00 EST |
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--- |
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### Hardware & Environment |
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* **Training Platform:** MacBook Pro (M1 Pro Chip) |
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* **Acceleration:** Apple Silicon Metal Performance Shaders (MPS) |
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* **Framework:** Ultralytics YOLOv11 |
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* **Total Epochs:** ACE-V1 (90) + Finetuning ACE-V1.1 (30) = **120 Total Epochs** |
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--- |
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### Key Improvements in V1.1 |
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* **False-Positive Rate:** Achieved **1.00 Specificity** on healthy brain scans. |
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* **Accuracy:** Verified **0.899 mAP@0.5** on the independent test set. |
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* **Performance:** Optimized for a high F1-score to ensure reliable clinical support. |
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--- |
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### Performance & Validation |
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| Metric | Value | |
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| :--- | :--- | |
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| **mAP50** | **0.925** | |
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| **Precision** | **91.1%** | |
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| **Recall** | **89.7%** | |
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| **Background Specificity** | **1.00 (Perfect)** | |
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--- |
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### Performance & Testing (Blind Test) |
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| Metric | Value | |
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| :--- | :--- | |
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| **mAP50** | **0.899** | |
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| **Precision** | **90.0%** | |
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| **Recall** | **83.8%** | |
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| **Background Specificity** | **1.00 (Perfect)** | |
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#### **Test Proof** |
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*Figure 1: Normalized Confusion Matrix showing perfect separation of healthy tissue (Background).* |
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*Figure 2: Precision-Recall curve confirming the 0.899 mAP score.* |
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> **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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--- |
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### Operational Guide |
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For the most reliable results, I recommend the following inference settings based on the F1-Confidence analysis: |
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* **Recommended Confidence:** `0.466` |
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* **Image Size:** `640x640` |
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--- |
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### Usage Guide |
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To run inference with the ACE-V1.1 weights, use the following snippet: |
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```python |
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from ultralytics import YOLO |
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# Load the ACE-V1.1 weights |
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model = YOLO('ACE-V1.1.pt') |
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# Run inference with the optimal threshold |
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results = model.predict(source='mri_scan.jpg', conf=0.466, save=True) |
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``` |
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--- |
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### Citation |
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@misc{bowman2026acev11, |
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author = {Bowman, Alexa}, |
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title = {ACE-V1.1: Optimized Brain Tumor Detection with 1.00 Background Specificity}, |
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year = {2026}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{[https://huggingface.co/LexBwmn/ACE-V1](https://huggingface.co/LexBwmn/ACE-V1)}}, |
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note = {Fine-tuned YOLO11 on the BRISC 2025 Dataset (arXiv:2506.14318)}, |
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version = {1.1.0}, |
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hash = {7d95e4e369f39149866c38d44aec0c668ad703147fd30b28df99e514e41fd853} |
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} |
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