5 papers
ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising
Xuepeng Liu, Zetong Liu, Renyiming Li +5
Low-dose computed tomography (LDCT) reduces radiation exposure but introduces stronger quantum noise, streak artifacts, and local texture degradation, which can obscure anatomical…
AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising
Xuepeng Liu, Ruili Li, Zetong Liu +5
Positron emission tomography (PET) provides essential functional information for disease assessment, however reducing injected activity or acquisition time produces low-dose (LD) P…
A Semi-Supervised Framework for Breast Ultrasound Segmentation with Training-Free Pseudo-Label Generation and Label Refinement
Ruili Li, Jiayi Ding, Ruiyu Li +7
Semi-supervised learning (SSL) has emerged as a promising paradigm for breast ultrasound (BUS) image segmentation, but it often suffers from unstable pseudo labels under extremely…
PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Kernel Convolutional Neural Networks for Single Channel Speech Enhancement
Zizhen Lin, Junyu Wang, Ruili Li +2
Single-channel speech enhancement is a challenging ill-posed problem focused on estimating clean speech from degraded signals. Existing studies have demonstrated the competitive pe…
Dense-TSNet: Dense Connected Two-Stage Structure for Ultra-Lightweight Speech Enhancement
Zizhen Lin, Yuanle Li, Junyu Wang +1
Speech enhancement aims to improve speech quality and intelligibility in noisy environments. Recent advancements have concentrated on deep neural networks, particularly employing t…