metadata
license: apache-2.0
tags:
- bone-age
- medical-imaging
- radiology
- pediatrics
- pytorch
- pytorch-lightning
datasets:
- rsna-bone-age
metrics:
- mae
BoneAge: Pediatric Bone Age Assessment
Predicts skeletal bone age (in months) from pediatric hand/wrist X-rays.
Model Details
- Architecture: ConvNeXt-Tiny (ImageNet-22k pretrained) + sex-aware regression head
- Input: 512x512 grayscale hand X-ray + patient sex
- Output: Bone age in months + uncertainty estimates
- Training data: RSNA Pediatric Bone Age Challenge (12,611 images)
- Validation MAE: 7.97 months (single model, no TTA)
Usage
pip install git+https://github.com/FlatNineOrg/medilab-boneage.git
boneage predict hand.png --sex male --uncertainty
from boneage.config import BoneAgeConfig
from boneage.inference.predictor import Predictor
predictor = Predictor(BoneAgeConfig()) # auto-downloads weights
result = predictor.predict("hand.png", sex=1)
print(f"Bone age: {result['predicted_age_months']:.1f} months")
Training
Trained for 46 epochs on a Tesla P100 GPU (Kaggle). ConvNeXt-Tiny backbone with:
- AdamW optimizer (lr=1e-4, backbone at 0.1x)
- Cosine annealing with 5-epoch warmup
- Mixed precision (fp16)
- Augmentation: rotation, scale, flip, brightness/contrast, Gaussian noise, coarse dropout
License
Apache 2.0