Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …

Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but such …

Deep learning models for semantic segmentation of images require large amounts of data.
In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling …

Federated learning (FL) is a computational paradigm that enables organizations to collaborate
on machine learning (ML) projects without sharing sensitive data, such as, patient records…

Although both natural and induced regulatory T (nTreg and iTreg) cells can enforce tolerance,
the mechanisms underlying their synergistic actions have not been established. We …

In addition to thymus-derived or natural T regulatory (nT reg) cells, a second subset of induced
T regulatory (iT reg) cells arises de novo from conventional CD4+ T cells in the periphery. …

Objective. Federated learning (FL) is a computational paradigm that enables organizations
to collaborate on machine learning (ML) and deep learning (DL) projects without sharing …

This manuscript describes the first challenge on Federated Learning, namely the Federated
Tumor Segmentation (FeTS) challenge 2021. International challenges have become the …

Wind erosion and blowing dust threaten food security, human health and ecosystem services
across global drylands. Monitoring wind erosion is needed to inform management, with …

The method of joinpoint regression has been used in numerous domains to assess changes
in time series data, including such things as cancer mortality rates, motor vehicle collision …