Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …

While neural fields have made significant strides in view synthesis and scene reconstruction,
editing them poses a formidable challenge due to their implicit encoding of geometry and …

The high performance of denoising diffusion models for image generation has paved the
way for their application in unsupervised medical anomaly detection. As diffusion-based …

Visual anomaly detection has emerged as a highly applicable solution in practical industrial
manufacturing, owing to its notable effectiveness and efficiency. However, it also presents …

Biomedical image segmentation plays a pivotal role in medical imaging, facilitating precise
identification and delineation of anatomical structures and abnormalities. This review …

Ensuring product quality through automated anomaly detection is crucial in manufacturing.
Traditional methods often struggle to capture both local and global features effectively …

Existing B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) for
developmental dysplasia of the hip (DDH) is mainly developed based on the Graf's method …

This article aims to provide insights into a challenging rubbing signal-recovering problem
that arises when analyzing acoustic signals caused by rubbing in multirotor systems. Since …

Diffusion models are a type of generative deep learning model that can process medical
images more efficiently than traditional generative models. They have been applied to …

Denoising diffusion probabilistic models (DDPMs) have achieved unprecedented success in
computer vision. However, they remain underutilized in medical imaging, a field crucial for …