Hospitals do not always have the opportunity to collect data in tidy, uniform batches. A clinic may have a handful of carefully labeled images from one scanner while holding thousands of unlabeled ...

In a recent study published in the journal Nature Medicine, researchers used diffusion models for data augmentation to increase the robustness and fairness of medical machine learning (ML) models in ...

A positive and unlabeled learning (PUL) problem occurs when a machine learning set of training data has only a few positive labeled items and many unlabeled items. PUL problems often occur with ...

Real-world data (RWD) derived from electronic health records (EHRs) are often used to understand population-level relationships between patient characteristics and cancer outcomes. Machine learning ...