Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

The landscape of publicly available vision foundation models (VFMs) such as CLIP and
SAM is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their …

Background Accurate segmentation of brain tumour in magnetic resonance images (MRI) is
a difficult task due to various tumour types. Using information and features from multimodal …

Performance of models highly depend not only on the used algorithm but also the data set it
was applied to. This makes the comparison of newly developed tools to previously …

Background and Purpose: The ISLES challenge (Ischemic Stroke Lesion Segmentation)
enables globally diverse teams to compete to develop advanced tools for stroke lesion …

Magnetic resonance imaging is the most generally utilized imaging methodology that
permits radiologists to look inside the cerebrum using radio waves and magnets for tumor …

Background Tumor segmentation of glioma on MRI is a technique to monitor, quantify and
report disease progression. Manual MRI segmentation is the gold standard but very labor …

Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …

Acute ischemic stroke is one of the leading causes of death and long-term disability
worldwide. It occurs when a blood clot blocks an artery that supplies blood to the brain …