Zobrazeno 1 - 10
of 914
pro vyhledávání: '"Yap, Pew Thian"'
Cone Beam Computed Tomography (CBCT) finds diverse applications in medicine. Ensuring high image quality in CBCT scans is essential for accurate diagnosis and treatment delivery. Yet, the susceptibility of CBCT images to noise and artifacts undermine
Externí odkaz:
http://arxiv.org/abs/2409.18355
Multi-site structural MRI is increasingly used in neuroimaging studies to diversify subject cohorts. However, combining MR images acquired from various sites/centers may introduce site-related non-biological variations. Retrospective image harmonizat
Externí odkaz:
http://arxiv.org/abs/2408.09315
Autor:
Bernardo, Danilo, Xie, Xihe, Verma, Parul, Kim, Jonathan, Liu, Virginia, Numis, Adam L., Wu, Ye, Glass, Hannah C., Yap, Pew-Thian, Nagarajan, Srikantan S., Raj, Ashish
Publikováno v:
Commun Phys 7, 255 (2024)
The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogr
Externí odkaz:
http://arxiv.org/abs/2405.02524
This study investigates the applicability of 3D dose predictions from a model trained on one modality to a cross-modality automated planning workflow. Additionally, we explore the impact of integrating a multi-criteria optimizer on adapting predictio
Externí odkaz:
http://arxiv.org/abs/2402.15466
Brain magnetic resonance imaging (MRI) has been extensively employed across clinical and research fields, but often exhibits sensitivity to site effects arising from non-biological variations such as differences in field strength and scanner vendors.
Externí odkaz:
http://arxiv.org/abs/2402.06875
Untrained networks inspired by deep image priors have shown promising capabilities in recovering high-quality images from noisy or partial measurements without requiring training sets. Their success is widely attributed to implicit regularization due
Externí odkaz:
http://arxiv.org/abs/2312.09988
Cortical surface reconstruction (CSR) from MRI is key to investigating brain structure and function. While recent deep learning approaches have significantly improved the speed of CSR, a substantial amount of runtime is still needed to map the cortex
Externí odkaz:
http://arxiv.org/abs/2312.05986
Publikováno v:
Pattern Recognition, volume 151, 2024
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, m
Externí odkaz:
http://arxiv.org/abs/2306.05980
Deep Image Prior (DIP) shows that some network architectures naturally bias towards smooth images and resist noises, a phenomenon known as spectral bias. Image denoising is an immediate application of this property. Although DIP has removed the requi
Externí odkaz:
http://arxiv.org/abs/2304.11409
Autor:
Chen, Xiaoyang, Wu, Jinjian, Lyu, Wenjiao, Zou, Yicheng, Thung, Kim-Han, Liu, Siyuan, Wu, Ye, Ahmad, Sahar, Yap, Pew-Thian
Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associ
Externí odkaz:
http://arxiv.org/abs/2301.01369