Radiotherapy planning of head and neck cancer patients requires an accurate delineation of
several organs at risk (OAR) from planning CT images in order to determine a dose plan …

Abstract Machine learning, especially deep learning, has achieved state-of-the-art
performance on various computer vision tasks. For computer vision tasks in the medical …

Federated deep learning is a method for training a deep learning neural network model on
vast amounts of privacy-sensitive patient-related data without having to exchange the data …

Accurate segmentation of brain tumours images is crucial for diagnosis, treatment planning,
and monitoring of disease progression. However, acquiring sufficient medical imaging data …

Federated learning (FL) has emerged as a promising strategy for collaboratively training
complicated machine learning models from different medical centers without the need of …

Segmentation of head and neck cancer (HNC) primary tumors on medical images is an
essential, yet labor-intensive, aspect of radiotherapy. PET/CT imaging offers a unique ability …

Medical image segmentation is a vital healthcare endeavor requiring precise and efficient
models for appropriate diagnosis and treatment. Vision transformer (ViT)-based …

Head and Neck (H&N) organ-at-risk (OAR) and tumor segmentations are an essential
component of radiation therapy planning. The varying anatomic locations and dimensions of …

A chief challenge of deep learning in computer-aided diagnosis is to collect a large
heterogeneous dataset from multiple hospitals for constructing a robust deep learning …

Accurate segmentation of primary tumors from Computed Tomography (CT) and Positron
Emission Tomography (PET) images is essential for Head-and-Neck (H&N) cancer …