Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging
research. The size and scope of the training image datasets needed for successful AI model …

Automatic segmentation of colon polyps can significantly reduce the misdiagnosis of colon
cancer and improve physician annotation efficiency. While many methods have been …

Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient
diagnosis and treatment. Although manual tracing is the current standard in clinics …

Background and purpose Magnetic resonance imaging (MRI)‐based synthetic computed
tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for …

Pancreas segmentation has been traditionally challenging due to its small size in computed
tomography abdominal volumes, high variability of shape and positions among patients, and …

Breast cancer is one of the most common cancers among women worldwide, with early
detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic …

Precise delineation of multiple organs or abnormal regions in the human body from medical
images plays an essential role in computer-aided diagnosis, surgical simulation, image …

This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-
DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition …

Background Clinical evidence has demonstrated that proton therapy can achieve
comparable tumor control probabilities compared to conventional photon therapy but with …

Background: Decoding emotions from brain maps is a challenging task. Convolutional
Neural Network (CNN) is commonly used for EEG feature map. However, due to its local …