Zobrazeno 1 - 10
of 2 169
pro vyhledávání: '"Nachev, A."'
Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific models due to the complex brain processing mechanisms and the variations i
Externí odkaz:
http://arxiv.org/abs/2412.19487
Autor:
Deltadahl, Simon, Gilbey, Julian, Van Laer, Christine, Boeckx, Nancy, Leers, Mathie, Freeman, Tanya, Aiken, Laura, Farren, Timothy, Smith, Matthew, Zeina, Mohamad, consortium, BloodCounts, Rudd, James HF, Piazzese, Concetta, Taylor, Joseph, Gleadall, Nicholas, Schönlieb, Carola-Bibiane, Sivapalaratnam, Suthesh, Roberts, Michael, Nachev, Parashkev
Accurate classification of haematological cells is critical for diagnosing blood disorders, but presents significant challenges for machine automation owing to the complexity of cell morphology, heterogeneities of biological, pathological, and imagin
Externí odkaz:
http://arxiv.org/abs/2408.08982
Autor:
Tangwiriyasakul, Chayanin, Borges, Pedro, Moriconi, Stefano, Wright, Paul, Mah, Yee-Haur, Teo, James, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Stroke is a leading cause of disability and death. Effective treatment decisions require early and informative vascular imaging. 4D perfusion imaging is ideal but rarely available within the first hour after stroke, whereas plain CT and CTA usually a
Externí odkaz:
http://arxiv.org/abs/2404.04025
Autor:
Ruffle, James K, Mohinta, Samia, Baruteau, Kelly Pegoretti, Rajiah, Rebekah, Lee, Faith, Brandner, Sebastian, Nachev, Parashkev, Hyare, Harpreet
The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly
Externí odkaz:
http://arxiv.org/abs/2404.15318
Autor:
Cardoso, M. Jorge, Moosbauer, Julia, Cook, Tessa S., Erdal, B. Selnur, Genereaux, Brad, Gupta, Vikash, Landman, Bennett A., Lee, Tiarna, Nachev, Parashkev, Somasundaram, Elanchezhian, Summers, Ronald M., Younis, Khaled, Ourselin, Sebastien, Pfister, Franz MJ
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology. Beginning with rigorous pre-deploymen
Externí odkaz:
http://arxiv.org/abs/2311.14570
Autor:
Ruffle, James K, Watkins, Henry, Gray, Robert J, Hyare, Harpreet, de Schotten, Michel Thiebaut, Nachev, Parashkev
The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as
Externí odkaz:
http://arxiv.org/abs/2310.16113
Autor:
Ruffle, James K, Gray, Robert J, Mohinta, Samia, Pombo, Guilherme, Kaul, Chaitanya, Hyare, Harpreet, Rees, Geraint, Nachev, Parashkev
Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications, and casting doubt on the generalisability of inferr
Externí odkaz:
http://arxiv.org/abs/2309.07096
Autor:
Nelson, Amy PK, Mole, Joe, Pombo, Guilherme, Gray, Robert J, Ruffle, James K, Chan, Edgar, Rees, Geraint E, Cipolotti, Lisa, Nachev, Parashkev
The quantification of cognitive powers rests on identifying a behavioural task that depends on them. Such dependence cannot be assured, for the powers a task invokes cannot be experimentally controlled or constrained a priori, resulting in unknown vu
Externí odkaz:
http://arxiv.org/abs/2308.07039
Autor:
Pinaya, Walter H. L., Graham, Mark S., Kerfoot, Eric, Tudosiu, Petru-Daniel, Dafflon, Jessica, Fernandez, Virginia, Sanchez, Pedro, Wolleb, Julia, da Costa, Pedro F., Patel, Ashay, Chung, Hyungjin, Zhao, Can, Peng, Wei, Liu, Zelong, Mei, Xueyan, Lucena, Oeslle, Ye, Jong Chul, Tsaftaris, Sotirios A., Dogra, Prerna, Feng, Andrew, Modat, Marc, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perfor
Externí odkaz:
http://arxiv.org/abs/2307.15208
Autor:
Graham, Mark S., Pinaya, Walter Hugo Lopez, Wright, Paul, Tudosiu, Petru-Daniel, Mah, Yee H., Teo, James T., Jäger, H. Rolf, Werring, David, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust way to perf
Externí odkaz:
http://arxiv.org/abs/2307.03777