Zobrazeno 1 - 10
of 11 726
pro vyhledávání: '"Fessler, A."'
Model-based iterative reconstruction plays a key role in solving inverse problems. However, the associated minimization problems are generally large-scale, ill-posed, nonsmooth, and sometimes even nonconvex, which present challenges in designing effi
Externí odkaz:
http://arxiv.org/abs/2411.08178
Diffusion models have achieved excellent success in solving inverse problems due to their ability to learn strong image priors, but existing approaches require a large training dataset of images that should come from the same distribution as the test
Externí odkaz:
http://arxiv.org/abs/2410.11730
Reconstructing 3D cone beam computed tomography (CBCT) images from a limited set of projections is an important inverse problem in many imaging applications from medicine to inertial confinement fusion (ICF). The performance of traditional methods su
Externí odkaz:
http://arxiv.org/abs/2410.10836
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views
Purpose: This study addresses the challenge of extended SPECT imaging duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by developing a self-supervised learning approach to synthesize skipped SPECT projection views, thus sh
Externí odkaz:
http://arxiv.org/abs/2406.18840
Diffusion models face significant challenges when employed for large-scale medical image reconstruction in real practice such as 3D Computed Tomography (CT). Due to the demanding memory, time, and data requirements, it is difficult to train a diffusi
Externí odkaz:
http://arxiv.org/abs/2406.10211
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and requires lots of data. Such bottlenecks prevent most existing works fro
Externí odkaz:
http://arxiv.org/abs/2406.02462
Autor:
Hickey, Daniel, Schmitz, Matheus, Fessler, Daniel M. T., Smaldino, Paul E., Lerman, Kristina, Murić, Goran, Burghardt, Keith
Counterspeech -- speech that opposes hate speech -- has gained significant attention recently as a strategy to reduce hate on social media. While previous studies suggest that counterspeech can somewhat reduce hate speech, little is known about its e
Externí odkaz:
http://arxiv.org/abs/2405.18374
Socio-linguistic indicators of text, such as emotion or sentiment, are often extracted using neural networks in order to better understand features of social media. One indicator that is often overlooked, however, is the presence of hazards within te
Externí odkaz:
http://arxiv.org/abs/2405.17838
Many online hate groups exist to disparage others based on race, gender identity, sex, or other characteristics. The accessibility of these communities allows users to join multiple types of hate groups (e.g., a racist community and a misogynistic co
Externí odkaz:
http://arxiv.org/abs/2405.17410
Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a
Externí odkaz:
http://arxiv.org/abs/2405.03854