Recently, advances in wearable technologies, data science and machine learning have
begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of …

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the
application of AI-based cancer imaging analysis to address other, more complex, clinical …

Biomedical imaging is a driver of scientific discovery and a core component of medical care
and is being stimulated by the field of deep learning. While semantic segmentation …

Glioblastomas are the most common form of malignant primary brain tumor and an important
cause of morbidity and mortality. In recent years there have been important advances in …

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified
nnU-Net baseline configuration already achieves a respectable result. By incorporating …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer
care. With recent advances in the field of artificial intelligence (AI), there is now a …

Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …

The most frequent adult-type primary CNS tumours are diffuse gliomas, but a large variety of
rarer CNS tumour types exists. The classification of these tumours is increasingly based on …