Pytorch implementation of the MICCAI 2020 paper SIMBA: Specific Identity Markers for Bone Age Assessment

Bone age estimation using hand X-Ray images

This is the Bone Age Assessment Dataset from RSNA Originally

Deep learning and segmentation in sex classification from left hand X-ray images in pediatric patients: how zero-shot Segment Anything Model (SAM) can improve medical image analysis

This is the Bone Age Assessment Dataset from Kaggle, But Enhanced by neu.ro

Develop a deep learning-based model utilizing a fully connected convolutional neural network (CNN) to predict bone age from left-hand radiographs.

The project is a collaboration with David Loaiza ( 4th Yr Radiologist) from Mexico at Cardiology national institute "Ignacio Chavez". The aim is to estimate the bone age from the left hand radiographs. The model will be trained on a RSNA Pediatric Bone Age Challenge (2017) public dataset and evaluated on private dataset obtained from the hospital.

Final group project of "Human Data Analytics" course at University of Padova