metadata
library_name: transformers
tags:
- radiology
- medical_imaging
- bone_age
- x_ray
- crop
license: apache-2.0
pipeline_tag: object-detection
base_model:
- timm/mobilenetv3_small_100.lamb_in1k
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)