metadata
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
Generated by ML Intern
This model repository was generated by 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
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.