The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …

Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We
demonstrate that large-scale pre-trained transformers can significantly improve the state-of …

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying
machine learning models in the real world. Previous methods commonly rely on an OOD …

The intraoperative activity of a surgeon has substantial impact on postoperative outcomes.
However, for most surgical procedures, the details of intraoperative surgical actions, which …

While neural networks achieve state-of-the-art performance for many molecular modeling
and structure–property prediction tasks, these models can struggle with generalization to out …

Artificial intelligence (AI) solutions that automatically extract information from digital histology
images have shown great promise for improving pathological diagnosis. Prior to routine use …

Generalization–the ability of AI systems to apply and/or extrapolate their knowledge to new
data which might differ from the original training data–is a major challenge for the effective …

Protein kinases regulate various cellular functions and hold significant pharmacological
promise in cancer and other diseases. Although kinase inhibitors are one of the largest …

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …