ianpan/bone-age-crop

This model crops hand radiographs to better standardize the image input for bone age models. The model uses a lightweight mobilenetv3_small_100 backbone and predicts normalized xywh coordinates.

The model was trained and validated using 12,592 pediatric hand radiographs from the RSNA Pediatric Bone Age Challenge using an 80%/20% split. On single-fold validation, the model achieved mean absolute errors (normalized coordinates) of:

x: 0.0152
y: 0.0121
w: 0.0261
h: 0.0213

To use the model:

import cv2
import torch
from transformers import AutoModel

model = AutoModel.from_pretrained("ianpan/bone-age-crop", trust_remote_code=True)
model = model.eval()
img = cv2.imread(..., 0)
img_shape = torch.tensor([img.shape[:2]])
x = model.preprocess(img)
x = torch.from_numpy(x).unsqueeze(0).unsqueeze(0)
x = x.float()

# if you do not provide img_shape
# model will return normalized coordinates
with torch.inference_mode():
  coords = model(x, img_shape)

# only 1 sample in batch
coords = coords[0].numpy()
x, y, w, h = coords
# coords already rescaled with img_shape
cropped_img = img[y: y + h, x: x + w]

If you have pydicom installed, you can also load a DICOM image directly:

img = model.load_image_from_dicom(path_to_dicom)