bone age EU AI Act Compliance Profile

This model has been trained and validated on 14,036 pediatric hand radiographs from the RSNA Pediatric Bone Age Challenge dataset, which is publicly available. It can be loaded using:

The model is a 3-fold ensemble utilizing the convnextv2_tiny backbone. The individual single-fold models can be accessed through model.net0, model.net1, model.net2. Each of these models was trained over 20,000 iterations using a batch size of 64 across 2 NVIDIA RTX 3090 GPUs. 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

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 from t

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Base Model