---
license: apache-2.0
tags:
  - bone-age
  - medical-imaging
  - tanner-whitehouse-2
  - efficientnet-b3
  - multi-task-learning
  - pediatric-radiology
language:
  - en
---

# BoneAgeTW2 — EfficientNet-B3 Multi-Head TW2 Stage Classifier

**Paper:** [arXiv:2607.23224](https://arxiv.org/abs/2607.23224)  
**Code:** [github.com/jmmana/BoneAgeTW2](https://github.com/jmmana/BoneAgeTW2)  
**Author:** Juan Manuel Castillo Pinto — Universidad de La Salle, Bogotá

---

## What is this?

A PyTorch checkpoint of the stage classifier from **BoneAgeTW2**, an end-to-end deep learning
system for automated **Tanner-Whitehouse 2 (TW2)** skeletal maturation assessment.

The model uses a shared **EfficientNet-B3** backbone with **20 independent classification heads**
(one per TW2 bone site) to simultaneously predict the maturation stage of each bone in a
hand radiograph.

## Model Performance (Validation, n=1,262 — RSNA Bone Age Dataset)

| Metric | Value |
|---|---|
| Mean exact accuracy (all 20 bones) | **65.82%** |
| Mean within-1 accuracy (all 20 bones) | **96.77%** |
| End-to-end MAE (RUS pathway) | **14.71 months** |
| Carpal bones exact accuracy | 65.1–84.4% |
| RUS long bones exact accuracy | 56.7–66.8% |

The within-1 accuracy of 96.77% falls within the human radiologist inter-rater range (King et al., 1994).

## Checkpoint

| File | Size | Epochs | Loss |
|---|---|---|---|
| `models/stage_classifier_epoch2.pt` | 72 MB | 2 | 0.9477 |

## Loading the Model

```python
import torch, timm
from huggingface_hub import hf_hub_download

# Download
ckpt_path = hf_hub_download("maktub83/BoneAgeTW2", "models/stage_classifier_epoch2.pt")
checkpoint = torch.load(ckpt_path, map_location="cpu")

# Rebuild backbone
backbone = timm.create_model("efficientnet_b3", pretrained=False, num_classes=0)
backbone.load_state_dict(checkpoint["backbone"])
backbone.eval()

# feat_dim = 1536
feat_dim = checkpoint["feat_dim"]

# Each head: Linear(feat_dim, n_stages)
# See github.com/jmmana/BoneAgeTW2/training/04_train_stage_classifier.py
# for build_model() and BONE_NAMES
```

## TW2 Bones (20 heads)

**RUS (13):** radius, ulna, mc1, mc3, mc5, pp1, pp3, pp5, mp3, mp5, dp1, dp3, dp5  
**Carpal (7):** capitate, hamate, triquetral, lunate, scaphoid, trapezoid, trapezium

## Pseudo-Label Method

Training labels were generated by **Gaussian inversion** of the published TW2 reference
distributions: for each bone, age, and sex, the MAP stage from `gaussian_params.json`
is assigned as the pseudo-label. This produces 252,220 annotations from 12,611 RSNA
radiographs without any manual effort.

## Citation

```bibtex
@article{castillo2026boneagetw2,
  author  = {Castillo~Pinto, Juan~Manuel},
  title   = {{BoneAgeTW2}: Automated Skeletal Maturation Assessment via the {Tanner-Whitehouse 2} Method, Deep Learning, and Clinical Report Generation with Distribution Curves},
  journal = {arXiv preprint arXiv:2607.23224},
  year    = {2026}
}
```