Daniel S. Seidman

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization

This paper introduces DISCOVER, a generative model that disentangles latent spaces to provide interpretable explanations for image-based classification models in in vitro fertilization.

Using unlabeled information of embryo siblings from the same cohort cycle to enhance in vitro fertilization implantation prediction

The paper uses sibling embryo data from the same IVF cohort to improve machine learning prediction of embryo implantation outcomes.