---
license: cc-by-4.0
tags:
- medical-imaging
- ultrasound
- thyroid
- classification
- resnet
- ml-intern
datasets:
- Johnyquest7/TN5000-thyroid-nodule-classification
---
# Thyroid Nodule Classification - ResNet-18 (PEMV-Style Correct)
Trained on TN5000 with exact PEMV paper recipe, optimized for AUC-ROC.

## Key Recipe Differences from Failed Runs
- No ImageNet normalization (only ToTensor to [0,1])
- CrossEntropyLoss with 2 logits (not BCE with 1 logit)
- ResNet-18 (proven 85.68% accuracy baseline on TN5000)
- AdamW lr=1e-4, wd=0.05, batch=16, 128x128
- Constant LR for 200 epochs (no scheduler)

## Test Set Performance
| Metric | Value | 95% CI |
|--------|-------|--------|
| Accuracy | 0.8891 | - |
| Sensitivity | 0.9175 | [0.8948, 0.9366] |
| Specificity | 0.8182 | [0.7685, 0.8611] |
| PPV | 0.9266 | [0.9048, 0.9447] |
| NPV | 0.7986 | [0.7481, 0.8430] |
| AUC-ROC | 0.9313 | [0.9125, 0.9483] |

## References
- PEMV-Thyroid (arXiv:2603.28315): Prototype-Enhanced Multi-View Learning for Thyroid Nodule Ultrasound Classification

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.

- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'Johnyquest7/Thyroid_EfficientNetV2'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
```

For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.