Behnam Javanmardi

Bone2Gene uses deep learning to classify rare bone diseases from hand radiographs, achieving high accuracy for distinct disorders but lower for overlapping ones.

This paper introduces GestaltGAN, a generative model that creates photorealistic portraits of individuals with rare genetic disorders while preserving privacy.

This study compares human and computer attention patterns when assessing images of genetic conditions using eye-tracking and deep learning saliency maps.

The paper introduces the GestaltMatcher Database (GMDB), a global facial phenotypic reference for rare diseases, showing improved NGP performance with diverse ancestry data.

GestaltMatcher improves diagnosis of ultra-rare genetic disorders by matching facial phenotypes to molecular diagnoses using a deep learning model trained on a large dataset of patient photographs.