Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the
potential for many fields—including medicine—to benefit from the insights that AI techniques …

Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …

Biomedical image analysis algorithm validation depends on high-quality annotation of
reference datasets, for which labelling instructions are key. Despite their importance, their …

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral
part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer …

Background Deep learning enables accurate high-resolution mapping of cells and tissue
structures that can serve as the foundation of interpretable machine-learning models for …

While artificial intelligence (AI) holds promise for supporting healthcare providers and
improving the accuracy of medical diagnoses, a lack of transparency in the composition of …

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to
obtain, particularly in medical imaging, where annotations also require expert knowledge …

Despite impressive developments in deep convolutional neural networks for medical
imaging, the paradigm of supervised learning requires numerous annotations in training to …

Crowdsourcing, a distributed human problem-solving paradigm is an active research area
which has attracted significant attention in the fields of computer science, business, and …