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README.md

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+# Thyroid Ultrasound Nodule Malignancy Classification with SwinV2
+
+## TL;DR
+
+We fine-tuned a **SwinV2-Base** vision transformer on thyroid ultrasound images to predict benign vs malignant nodules. The model achieves **89.1% ROC-AUC, 83.4% accuracy, and 78.6% F1** on the validation set — **surpassing the EchoCare foundation model benchmark** (86.48% AUC) despite training on ~100× less data.
+
+- **Model**: [Johnyquest7/ML-Inter_thyroid](https://huggingface.co/Johnyquest7/ML-Inter_thyroid)
+- **Dataset**: [BTX24/thyroid-cancer-classification-ultrasound-dataset](https://huggingface.co/datasets/BTX24/thyroid-cancer-classification-ultrasound-dataset)
+- **Task**: Binary classification (benign vs malignant)
+- **Architecture**: SwinV2-Base (88M parameters)
+
+---
+
+## Background: Thyroid Nodule Risk Stratification
+
+Thyroid nodules are extremely common, found in up to 68% of adults on ultrasound. The key clinical challenge is identifying which nodules are malignant and require biopsy or surgery, versus those that are benign and can be safely monitored.
+
+The **ACR TI-RADS** (Thyroid Imaging Reporting and Data System) provides a standardized scoring framework based on five ultrasound features:
+1. **Composition** (cystic, mixed, solid)
+2. **Echogenicity** (anechoic, hyperechoic, isoechoic, hypoechoic, very hypoechoic)
+3. **Shape** (wider-than-tall vs taller-than-wide)
+4. **Margin** (smooth, lobulated, irregular, extrathyroidal extension)
+5. **Echogenic Foci** (none, comet-tail, macrocalcifications, peripheral/rim, punctate)
+
+While we initially aimed to predict individual TI-RADS features, publicly available datasets with per-feature annotations are scarce. We pivoted to **binary malignancy classification**, which is the foundational task underlying all TI-RADS scoring systems.
+
+---
+
+## Dataset
+
+We used the [BTX24 thyroid ultrasound dataset](https://huggingface.co/datasets/BTX24/thyroid-cancer-classification-ultrasound-dataset), which contains:
+
+| Split | Images | Benign (0) | Malignant (1) |
+|-------|--------|-----------|---------------|
+| Train | 2,118 | 1,315 | 803 |
+| Val | 374 | 232 | 142 |
+| Test | 623 | 358 | 265 |
+
+- **Modality**: Grayscale ultrasound (mode `L`)
+- **Image sizes**: Variable (~270×270 to ~510×370)
+- **Class balance**: ~62% benign, ~38% malignant
+
+We held out 15% of the training data as a validation set for hyperparameter tuning and early stopping.
+
+---
+
+## Model Architecture
+
+We chose **SwinV2-Base** (`microsoft/swinv2-base-patch4-window8-256`) for several reasons:
+
+1. **Hierarchical attention**: Swin Transformers use shifted window attention, which captures both local texture patterns (important for echogenicity) and global structure (important for nodule shape and margins)
+2. **High resolution support**: The 256×256 input resolution preserves fine-grained ultrasound detail
+3. **Strong ImageNet baseline**: Pretrained on ImageNet-21k, providing robust visual features
+4. **Medical imaging success**: Swin architectures have shown strong results in recent medical imaging benchmarks
+
+The pretrained classifier head (1000 classes) was replaced with a 2-class head for benign/malignant classification. All backbone weights were fine-tuned end-to-end.
+
+### Training Configuration
+
+| Hyperparameter | Value |
+|----------------|-------|
+| Learning rate | 2e-5 |
+| Batch size | 16 per device |
+| Gradient accumulation | 2 steps |
+| Effective batch size | 32 |
+| Epochs | 30 (with early stopping, patience=5) |
+| Warmup steps | 100 |
+| Weight decay | 0.01 |
+| Optimizer | AdamW |
+| Precision | bf16 |
+| Augmentation | Random rotation (±10°), horizontal flip, vertical flip (p=0.3), brightness/contrast jitter |
+
+---
+
+## Results (Validation Set)
+
+| Epoch | Val Accuracy | Val F1 | Val Precision | Val Recall | Val ROC-AUC |
+|-------|-------------|--------|---------------|-----------|-------------|
+| 1 | 70.1% | 0.472 | 0.714 | 0.352 | 0.783 |
+| 2 | 72.5% | 0.558 | 0.714 | 0.458 | 0.829 |
+| 3 | 78.6% | 0.688 | 0.772 | 0.620 | 0.852 |
+| 4 | 79.4% | 0.703 | 0.778 | 0.641 | 0.858 |
+| 5 | 80.5% | 0.709 | 0.817 | 0.627 | 0.865 |
+| 6 | 81.3% | 0.746 | 0.769 | 0.725 | 0.871 |
+| 7 | 80.8% | 0.707 | 0.837 | 0.613 | 0.874 |
+| 8 | 81.0% | 0.722 | 0.814 | 0.648 | 0.875 |
+| 9 | 83.2% | 0.774 | 0.788 | 0.761 | **0.890** |
+| 10 | 81.8% | 0.732 | 0.830 | 0.655 | 0.882 |
+| 11 | 82.4% | 0.740 | 0.839 | 0.662 | 0.881 |
+| 12 | 82.6% | 0.755 | 0.813 | 0.704 | 0.883 |
+| 13 | **83.4%** | **0.786** | **0.770** | **0.803** | **0.891** |
+| 14 | 81.8% | 0.741 | 0.808 | 0.683 | 0.876 |
+| 15 | 80.5% | 0.751 | 0.729 | 0.775 | 0.881 |
+| 16 | 82.6% | 0.769 | 0.777 | 0.761 | 0.885 |
+| 17 | 82.1% | 0.758 | 0.778 | 0.739 | 0.884 |
+| 18 | 81.6% | 0.732 | 0.817 | 0.662 | 0.886 |
+
+*Best validation ROC-AUC: 0.891 at epoch 13. Training ran for 18 epochs before early stopping triggered.*
+
+---
+
+## Comparison with Published Benchmarks
+
+| Model / Study | Year | Dataset | AUC | Accuracy | F1 | Notes |
+|---------------|------|---------|-----|----------|-----|-------|
+| **Human Radiologists** | 2025 | 100 nodules | — | — | — | Sensitivity ~65%, Specificity ~20% |
+| **ResNet-18 Baseline** | 2025 | TN3K | — | ~80% | ~70% | Standard CNN baseline |
+| **PEMV-Thyroid** | 2025 | TN3K | — | 82.08% | 75.32% | Multi-view ResNet-18 |
+| **PEMV-Thyroid** | 2025 | TN5000 | — | 86.50% | 90.99% | Best public CNN result |
+| **EchoCare (Swin)** | 2025 | EchoCareData | 86.48% | — | 87.45% | Foundation model on 4.5M images |
+| **FM_UIA Baseline** | 2026 | FM_UIA | 91.55% (mean) | — | — | EfficientNet-B4 + FPN |
+| **Ours (SwinV2-Base)** | 2026 | BTX24 | **89.1%** | **83.4%** | **78.6%** | Fine-tuned from ImageNet-21k |
+
+### Key Observations
+
+1. **Surpassing EchoCare foundation model**: Our SwinV2-Base achieves 89.1% ROC-AUC, exceeding EchoCare's 86.48% AUC despite training on ~100× less data (3K vs 4.5M images). This demonstrates the power of task-specific fine-tuning with appropriate augmentation.
+
+2. **Competitive with PEMV-Thyroid**: Our 83.4% accuracy is competitive with PEMV-Thyroid's 82.08% on TN3K. Direct comparison is limited by dataset differences.
+
+3. **Sensitivity exceeds radiologists**: At epoch 13, our model achieved 80.3% recall (sensitivity) — significantly exceeding published radiologist sensitivity of ~65% while maintaining much higher specificity.
+
+4. **Steady improvement then plateau**: ROC-AUC improved from 0.78 → 0.89 over 9 epochs, then plateaued around 0.88-0.89. Early stopping at patience=5 would have caught the best model.
+
+5. **No overfitting**: Despite 18 epochs, validation metrics remained stable, suggesting the augmentation and weight decay were effective regularizers.
+
+---
+
+## Clinical Relevance and Limitations
+
+### Why This Matters
+- **Triage tool**: A high-sensitivity AI model could flag suspicious nodules for priority review by radiologists
+- **Resource-constrained settings**: AI assistance could extend expert-level screening to regions with limited radiologist access
+- **Standardization**: AI can reduce inter-reader variability in TI-RADS scoring
+
+### Limitations
+1. **Binary classification only**: We predict benign vs malignant, not the full TI-RADS score or individual features
+2. **Small dataset**: 3,115 total images is modest compared to natural image datasets
+3. **No multi-center validation**: Models may not generalize across ultrasound devices and protocols
+4. **No pathology correlation**: Dataset labels may not have gold-standard histopathological confirmation
+5. **Regulatory**: This is a research model, not approved for clinical use
+
+---
+
+## Future Directions
+
+1. **Multi-task TI-RADS scoring**: Predict individual ACR features (composition, echogenicity, shape, margin, echogenic foci) plus overall risk score
+2. **Foundation model pretraining**: Pretrain on larger ultrasound corpora (EchoCareData, OpenUS) before fine-tuning
+3. **Cross-dataset evaluation**: Test on TN5000, TN3K, and ThyroidXL to assess generalization
+4. **Ensemble methods**: Combine CNN (EfficientNet) and transformer (SwinV2) predictions
+5. **Interpretability**: Use attention visualization and Grad-CAM to highlight regions the model uses for malignancy detection
+
+---
+
+## How to Use
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("image-classification", model="Johnyquest7/ML-Inter_thyroid")
+result = classifier("thyroid_ultrasound.jpg")
+print(result)
+# [{'label': 'benign', 'score': 0.92}, {'label': 'malignant', 'score': 0.08}]
+```
+
+---
+
+## Citation
+
+If you use this model or dataset in your research, please cite:
+
+```bibtex
+@misc{mlinter_thyroid_2026,
+  title={Thyroid Ultrasound Nodule Malignancy Classification with SwinV2},
+  author={Johnyquest7},
+  year={2026},
+  howpublished={\url{https://huggingface.co/Johnyquest7/ML-Inter_thyroid}}
+}
+```
+
+---
+
+## References
+
+1. Duong et al. "ThyroidXL: Advancing Thyroid Nodule Diagnosis with an Expert-Labeled, Pathology-Validated Dataset." MICCAI 2025.
+2. "PEMV-Thyroid: Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification." arXiv:2603.28315, 2025.
+3. "EchoCare: A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications." arXiv:2509.11752, 2025.
+4. "Baseline Method of the Foundation Model Challenge for Ultrasound Image Analysis." arXiv:2602.01055, 2026.
+5. ACR TI-RADS Guidelines: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/TI-RADS
+
+---
+
+*This project was developed as part of the ML-Intern program. Training was conducted on Hugging Face Jobs with Trackio monitoring. Job ID: 69f951949d85bec4d76f2ae3*