Citation (TBD by 2025)

@article{mollineda2025sam,
  title={Sex classification from hand X-ray images in pediatric patients: how zero-shot Segment Anything Model (SAM) can improve medical image analysis},
  author={Ramón A. Mollineda, Becerra Karel, Mederos Boris},
  journal={Computers in Biology and Medicine},
  year={2025 TBD}
}

This work includes three primary steps: Segmentaion, Classification, and Visualization

Datasets (available in Kaggle)

Segmentation, Classification, Visualization

This work includes three primary steps: Segmentaion, Classification, and Visualization

Segmentation

(documentaion in progress: how segmentaion works) First step is to apply segmentation (SAM) on x-ray hand images Segmentation example outcome

Classification

With different variations of segmented images we proceed to classification:

Training

(inprogress: description on how to training)

Inference

(inprogress: description on how to run inference)

Visualization

Finally applying CAMs the visualization of results

Utils

Find Kaggle and Google Colab notebooks ready to vizualice datasets: check kaggle and google-colab folders

Kaggle

Notebooks ready to run on Kaggle environment

Google Colab

Notebooks ready to run on Google Colab environment