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 …

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

Objective. De-centralized data analysis becomes an increasingly preferred option in the
healthcare domain, as it alleviates the need for sharing primary patient data across …

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

Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …

In recent years, data-driven machine learning (ML) methods have revolutionized the
computer vision community by providing novel efficient solutions to many unsolved …

Background One approach to improving the personalized treatment of cancer is to
understand the cellular signaling transduction pathways that cause cancer at the level of the …

With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning
(ML), and Deep Learning (DL) algorithms, the landscape of data-driven medical applications …

Purpose Gliomas are the most common primary tumor of the brain and are classified into
grades IIV of the World Health Organization (WHO), based on their invasively histological …

Smart healthcare relies on artificial intelligence (AI) functions for learning and analysis of
patient data. Since large and diverse datasets for training of Machine Learning (ML) models …