ianpan
/

bone-age

@@ -1,199 +1,117 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- radiology
+- medical_imaging
+- bone_age
+- x_ray
+license: apache-2.0
+base_model:
+- timm/convnextv2_tiny.fcmae_ft_in22k_in1k
+pipeline_tag: image-classification
 ---
+This model has been trained and validated on 14,036 pediatric hand radiographs from the [RSNA Pediatric Bone Age Challenge](https://www.rsna.org/rsnai/ai-image-challenge/rsna-pediatric-bone-age-challenge-2017) dataset, which is publicly available.
+It can be loaded using:
+```
+from transformers import AutoModel
+model = AutoModel.from_pretrained("ianpan/bone-age", trust_remote_code=True)
+```
+The model is a 3-fold ensemble utilizing the `convnextv2_tiny` backbone.
+The individual models can be accessed through `model.net0`, `model.net1`, `model.net2`.
+Originally, it was trained with both a regression and classification head.
+However, this model only loads the classification head, as stand-alone performance was slightly better. The classification head also generates better GradCAMs.
+The softmax function is applied to the output logits and multiplied by the corresponding class indices, then summed.
+This outputs a scalar float value representing the predicted bone age in units of months.
+In addition to standard data augmentation, additional augmentations were also applied:
+- Using a cropped radiograph (from the model <https://huggingface.co/ianpan/bone-age-crop>) with probability 0.5
+- Histogram matching with a reference image (available in this repo under Files, `ref_img.png`) with probability 0.5
+The model was trained over 20,000 iterations using a batch size of 64 across 2 NVIDIA RTX 3090 GPUs.
+Note that both of the above augmentations could be applied simultaneously and in conjunction with standard data augamentations. Thus, the model accommodates a large range of variability in the appearance of a hand radiograph.
+On the original challenge test set comprising 200 multi-annotated pediatric hand radiographs, this model achieves a **mean absolute error of 4.16 months** (when applying both cropping and histogram matching to the input radiograph), which surpasses the [top solutions](https://pubs.rsna.org/doi/10.1148/radiol.2018180736) from the original challenge.
+Specific results as follows, with single model performance using `model.net0` in brackets:
+```
+Crop (-) / Histogram Matching (-): 4.42 [4.67] months
+Crop (+) / Histogram Matching (-): 4.47 [4.84] months
+Crop (-) / Histogram Matching (+): 4.34 [4.59] months
+Crop (+) / Histogram Matching (+): 4.16 [4.45] months
+```
+Thus it is preferable to both crop and histogram match the image to obtain the optimal results. See <https://huggingface.co/ianpan/bone-age-crop> for how to crop a bone age radiograph with a pretrained model.
+To histogram match with a reference image:
+```
+import cv2
+from skimage.exposure import match_histograms
+x = cv2.imread("target_radiograph.png", 0)
+ref = cv2.imread("ref_img.png", 0) # download ref_img.png from this repo
+x = match_histograms(x, ref)
+```
+Patient sex is an important variable affecting the model's prediction. This is passed to the model's `forward()` function using the `female` argument:
+```
+# 1 indicates female, 0 male
+model(x, female=torch.tensor([1, 0, 1, 0])) # assuming batch size of 4
+```
+Example usage for a single image:
+```
+import cv2
+import torch
+from skimage.exposure import match_histograms
+from transformers import AutoModel
+device = "cuda" if torch.cuda.is_available() else "cpu"
+crop_model = AutoModel.from_pretrained("ianpan/bone-age-crop", trust_remote_code=True)
+crop_model = crop_model.eval().to(device)
+img = cv2.imread(..., 0)
+img_shape = torch.tensor([img.shape[:2]])
+x = crop_model.preprocess(img) # only takes single image as input
+x = torch.from_numpy(x).unsqueeze(0).unsqueeze(0) # add channel, batch dims
+x = x.float()
+# if you do not provide img_shape
+# model will return normalized coordinates
+with torch.inference_mode():
+  coords = model(x.to(device), img_shape.to(device))
+# only 1 sample in batch
+coords = coords[0].cpu().numpy()
+x, y, w, h = coords
+# coords already rescaled with img_shape
+cropped_img = img[y: y + h, x: x + w]
+model = AutoModel.from_pretrained("ianpan/bone-age", trust_remote_code=True)
+model = model.eval().to(device)
+x = model.preprocess(cropped_img)
+x = torch.from_numpy(x).unsqueeze(0).unsqueeze(0)
+x = x.float()
+female = torch.tensor([1])
+with torch.inference_mode():
+  bone_age = model(x.to(device), female.to(device))
+```
+If you want the raw logits (class `i` = `i` months), you can pass `return_logits=True` to `forward()`:
+```
+bone_age_logits = model(x, female, return_logits=True)
+```
+To run single model inference, simply access one of the nets:
+```
+bone_age = model.net0(x, female)
+```
+If you have `pydicom` installed, you can also load a DICOM image directly:
+```
+img = model.load_image_from_dicom(path_to_dicom)
+```
+This model is for demonstration and research purposes only and has NOT been approved by any regulatory agency for clinical use.
+The user assumes any and all responsibility regarding their own use of this model and its outputs.