In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified …

Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie, …

Automatic segmentation of the liver and hepatic lesions is an important step towards
deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision …

Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding
normal brain tissue beyond the lesion outlines visible in the current medical scans. These …

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Increased intracranial pressure is the source of most critical symptoms in patients with
glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive …

Despite great advances in brain tumor segmentation and clear clinical need, translation of
state-of-the-art computational methods into clinical routine and scientific practice remains a …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS),
which was organized in conjunction with the IEEE International Symposium on …

Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the
primary anatomical site of tumour origin cannot be determined 1 , 2 . This poses a …

Despite increasing numbers of regulatory approvals, deep learning-based computational
pathology systems often overlook the impact of demographic factors on performance, …