Arıyan Teimoori

The paper introduces HTT, a model that improves cancer grade prediction from pathology reports by incorporating temporal information and cross-cancer transfer learning, achieving high AUROC scores on unseen cancer types.

The paper uses machine learning to analyze a dataset of electrospun nanofibers, finding that medicament identity, not total loading, better predicts fiber morphology in regenerative endodontics.