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pro vyhledávání: '"Alexander, A. C."'
Autor:
Xu, Moucheng, Zhou, Yukun, Goodwin-Allcock, Tobias, Firoozabadi, Kimia, Jacob, Joseph, Alexander, Daniel C., Slator, Paddy J.
We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential to robustly and repeatably measure biologically-relevant tissue maps t
Externí odkaz:
http://arxiv.org/abs/2411.10772
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Image Quality Transfer (IQT) aims to enhance the contrast and resolution of low-quality medical images, e.g. obtained from low-power devices, with rich information learned from higher quality images. In contrast to existing IQT methods which adopt su
Externí odkaz:
http://arxiv.org/abs/2411.05885
Disease progression models infer group-level temporal trajectories of change in patients' features as a chronic degenerative condition plays out. They provide unique insight into disease biology and staging systems with individual-level clinical util
Externí odkaz:
http://arxiv.org/abs/2410.14388
Autor:
Abdulaal, Ahmed, Fry, Hugo, Montaña-Brown, Nina, Ijishakin, Ayodeji, Gao, Jack, Hyland, Stephanie, Alexander, Daniel C., Castro, Daniel C.
Radiological services are experiencing unprecedented demand, leading to increased interest in automating radiology report generation. Existing Vision-Language Models (VLMs) suffer from hallucinations, lack interpretability, and require expensive fine
Externí odkaz:
http://arxiv.org/abs/2410.03334
Autor:
Zeinoddin, Mona Sheikh, Lena, Chiara, Qu, Jiongqi, Carlini, Luca, Magro, Mattia, Kim, Seunghoi, De Momi, Elena, Bano, Sophia, Grech-Sollars, Matthew, Mazomenos, Evangelos, Alexander, Daniel C., Stoyanov, Danail, Clarkson, Matthew J., Islam, Mobarakol
Robotic-assisted surgery (RAS) relies on accurate depth estimation for 3D reconstruction and visualization. While foundation models like Depth Anything Models (DAM) show promise, directly applying them to surgery often yields suboptimal results. Full
Externí odkaz:
http://arxiv.org/abs/2408.17433
Quantitative magnetic resonance imaging (qMRI) is increasingly investigated for use in a variety of clinical tasks from diagnosis, through staging, to treatment monitoring. However, experiment design in qMRI, the identification of the optimal acquisi
Externí odkaz:
http://arxiv.org/abs/2408.11834
Autor:
Cicimen, Alp G., Tregidgo, Henry F. J., Figini, Matteo, Messaritaki, Eirini, McNabb, Carolyn B., Palombo, Marco, Evans, C. John, Cercignani, Mara, Jones, Derek K., Alexander, Daniel C.
Prior work on the Image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultra-high resolution Diffusion MRI scans poses a problem in training neur
Externí odkaz:
http://arxiv.org/abs/2408.03216
In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep gene
Externí odkaz:
http://arxiv.org/abs/2405.03328
Autor:
Kim, Seunghoi, Jin, Chen, Diethe, Tom, Figini, Matteo, Tregidgo, Henry F. J., Mullokandov, Asher, Teare, Philip, Alexander, Daniel C.
Recent developments in diffusion models have advanced conditioned image generation, yet they struggle with reconstructing out-of-distribution (OOD) images, such as unseen tumors in medical images, causing "image hallucination" and risking misdiagnosi
Externí odkaz:
http://arxiv.org/abs/2404.05980
Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT), yet they struggle to generalize in uncalibrated modalities -- notably magnetic resonance (MR) imaging, where performance
Externí odkaz:
http://arxiv.org/abs/2311.16914