Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential
growth in recent years. The scientific community has focused its attention on DL due to its …

The interest for machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …

Automatic and accurate prostate ultrasound segmentation is a long-standing and
challenging problem due to the severe noise and ambiguous/missing prostate boundaries …

Purpose Automatic segmentation of medical images with deep learning (DL) algorithms has
proven highly successful in recent times. With most of these automation networks, inter …

Autosegmentation of gross tumor volumes holds promise to decrease clinical demand and
to provide consistency across clinicians and institutions for radiation treatment planning …

Purpose Providing the confidence level (Uncertainty) of prediction results and guiding
patient-specific quality assurance (pQA) can enhance the safety of AI (Artificial intelligence) …

Purpose Task automation is essential for efficient and consistent image segmentation in
radiation oncology. We report on a deep learning architecture, comprising a U‐Net and a …

Federated learning (FL) can collaboratively train deep learning models using isolated
patient data owned by different hospitals for various clinical applications, including medical …

Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that
incorporates progressive changes in patient anatomy into active plan/dose adaption during …

Background and purpose Convolutional neural networks (CNNs) have comparable human
level performance in automatic segmentation. An important challenge that CNNs face in …