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| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - medical |
| - thyroid |
| - ultrasound |
| - convnext |
| - explainable-ai |
| metrics: |
| - auc: 0.9389 |
| - accuracy: 0.8697 |
| --- |
| # Thyroid Nodule Malignancy Detector (5-Fold Validated) |
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| This model uses a **Dual-Stream Spatial-Frequency Fusion architecture** (ConvNeXt-Tiny + FFT Magnitude Spectrum) to classify thyroid nodules in ultrasound images. |
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| ## 📊 5-Fold Cross-Validation Performance |
| The model was evaluated using a stratified 5-fold cross-validation on the consolidated dataset. |
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| | Fold | Accuracy | AUC | Sensitivity | Specificity | |
| |-------:|-----------:|---------:|--------------:|--------------:| |
| | 0 | 0.881728 | 0.94519 | 0.92364 | 0.79386 | |
| | 1 | 0.866856 | 0.939398 | 0.878661 | 0.842105 | |
| | 2 | 0.869688 | 0.945065 | 0.899582 | 0.807018 | |
| | 3 | 0.871013 | 0.934528 | 0.900628 | 0.808791 | |
| | 4 | 0.858965 | 0.930304 | 0.875523 | 0.824176 | |
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| ### Summary Statistics: |
| - **Mean AUC:** 0.9389 ± 0.0065 |
| - **Mean Sensitivity:** 0.8956 ± 0.0195 |
| - **Mean Specificity:** 0.8152 ± 0.0185 |
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| ## 🚀 Clinical Application |
| The weights hosted here (pytorch_model.bin) correspond to **Fold 0**, which achieved the highest individual AUC of 0.9452. |
| |
| ## 🛠 Methodology |
| - **Backbone:** ConvNeXt-Tiny (Spatial Stream) |
| - **Texture Analysis:** FFT Magnitude Spectrum (Frequency Stream) |
| - **Preprocessing:** CLAHE |
| - **Loss Function:** Focal Loss (α=1, γ=2) |
| |
