Zobrazeno 1 - 10
of 979
pro vyhledávání: '"Aggelos K. Katsaggelos"'
Autor:
Sumra Bari, Byoung-Woo Kim, Nicole L. Vike, Shamal Lalvani, Leandros Stefanopoulos, Nicos Maglaveras, Martin Block, Jeffrey Strawn, Aggelos K. Katsaggelos, Hans C. Breiter
Publikováno v:
npj Mental Health Research, Vol 3, Iss 1, Pp 1-16 (2024)
Abstract Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict f
Externí odkaz:
https://doaj.org/article/942746ea20ae4cb3bb6b1afd40932db7
Autor:
Srutarshi Banerjee, Miesher Rodrigues, Manuel Ballester, Alexander H. Vija, Aggelos K. Katsaggelos
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Room temperature semiconductor radiation detectors (RTSD) for X-ray and $$\gamma$$ γ -ray detection are vital tools for medical imaging, astrophysics and other applications. CdZnTe (CZT) has been the main RTSD for more than three decades wi
Externí odkaz:
https://doaj.org/article/79569c84f1404224b9a716cef5684113
Publikováno v:
IEEE Access, Vol 12, Pp 127368-127379 (2024)
This paper presents a novel variational deep-learning approach for video atmospheric turbulence correction. We modify and tailor the Nonlinear Activation Free Network (NAFNet) architecture for video restoration, introducing a new transformer-based ch
Externí odkaz:
https://doaj.org/article/8903b41a58e14a24961ffa4daa917979
Autor:
Petros Nyfantis, Pablo Ruiz Mataran, Hector Nistazakis, George Tombras, Aggelos K. Katsaggelos
Publikováno v:
Journal of Imaging, Vol 10, Iss 10, p 249 (2024)
Phase Retrieval is defined as the recovery of a signal when only the intensity of its Fourier Transform is known. It is a non-linear and non-convex optimization problem with a multitude of applications including X-ray crystallography, microscopy and
Externí odkaz:
https://doaj.org/article/8c06c324d1874d099e5529f94bff83ff
Autor:
Nicole L. Vike, Sumra Bari, Byoung Woo Kim, Aggelos K. Katsaggelos, Anne J. Blood, Hans C. Breiter
Publikováno v:
PLoS ONE, Vol 19, Iss 3 (2024)
Externí odkaz:
https://doaj.org/article/150466b30f0e43f689374bd3f818b830
Autor:
Miguel Lopez-Perez, Pablo Morales-Alvarez, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos
Publikováno v:
IEEE Access, Vol 11, Pp 6922-6934 (2023)
Machine learning (ML) methods often require large volumes of labeled data to achieve meaningful performance. The expertise necessary for labeling data in medical applications like pathology presents a significant challenge in developing clinical-grad
Externí odkaz:
https://doaj.org/article/78e821b163a84d5ebc1266c2dcb2f6b3
Autor:
Srutarshi Banerjee, Miesher Rodrigues, Manuel Ballester, Alexander Hans Vija, Aggelos K. Katsaggelos
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Room-temperature semiconductor radiation detectors (RTSD) have broad applications in medical imaging, homeland security, astrophysics and others. RTSDs such as CdZnTe, CdTe are often pixelated, and characterization of these detectors at micr
Externí odkaz:
https://doaj.org/article/585a50dc292640d9870312639348d13b
Autor:
Yunan Wu, Pierre Besson, Emanuel A. Azcona, S. Kathleen Bandt, Todd B. Parrish, Hans C. Breiter, Aggelos K. Katsaggelos
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-16 (2022)
Abstract The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (
Externí odkaz:
https://doaj.org/article/029bf894439c460eb5cbaa76d920349d
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Abstract Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. O
Externí odkaz:
https://doaj.org/article/42170df63d0145a4ba82f4390d2b4467
Autor:
Miguel López-Pérez, Mohamed Amgad, Pablo Morales-Álvarez, Pablo Ruiz, Lee A. D. Cooper, Rafael Molina, Aggelos K. Katsaggelos
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Abstract The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy cr
Externí odkaz:
https://doaj.org/article/6f4708f23d7141a982a40bf1745ac009