Zobrazeno 1 - 10
of 8 291
pro vyhledávání: '"A. Hajnal"'
Publikováno v:
European Psychiatry, Vol 67, Pp S746-S746 (2024)
Introduction Mentalizing helps us to understand the behaviour of others in our everyday social interactions. Spontaneous mentalizing without explicit instructions refers to representing mental state attribution. Several studies have described social
Externí odkaz:
https://doaj.org/article/38ee47b759294736b76e56ecedfcc9d6
Autor:
Verdera, Jordina Aviles, Silva, Sara Neves, Tomi-Tricot, Raphael, Hall, Megan, Story, Lisa, Malik, Shaihan J, Hajnal, Joseph V, Rutherford, Mary A, Hutter, Jana
Purpose: To provide real-time quantitative organ-specific information - specifically placental and brain T2* - to allow optimization of the MR examination to the individual patient. Methods: A FIRE-based real-time setup segmenting placenta and fetal
Externí odkaz:
http://arxiv.org/abs/2409.16878
Autor:
T. Csulak, A. Hajnal, S. Kiss, F. Dembrovszky, Z. Sipos, M. Varjú-Solymár, M. Kovács, M. Herold, E. Varga, P. Hegyi, T. Tényi, R. Herold
Publikováno v:
European Psychiatry, Vol 65, Pp S113-S114 (2022)
Introduction Everyday social interactions are based on Theory of Mind (ToM) or mentalizing, whose complex processes are involved in understanding, representing one’s own and other people’s mental states. ToM is supposed to have two systems. The i
Externí odkaz:
https://doaj.org/article/86245ebcc44b4a37bfeb80a55ddaa022
Publikováno v:
European Psychiatry, Vol 65, Pp S199-S199 (2022)
Introduction Theory of Mind is the ability to attribute mental states to others. Investigations have distinguished implicit and explicit forms of ToM. It is known, that patients with schizoprenia have deficits in their explicit ToM, and they also sho
Externí odkaz:
https://doaj.org/article/9bfb77191a5c44319f3c394c0b8ae40a
Autor:
Silva, Sara Neves, Woodgate, Tomas, McElroy, Sarah, Cleri, Michela, Clair, Kamilah St, Verdera, Jordina Aviles, Payette, Kelly, Uus, Alena, Story, Lisa, Lloyd, David, Rutherford, Mary A, Hajnal, Joseph V, Pushparajah, Kuberan, Hutter, Jana
Two subsequent deep learning networks, one localizing the fetal chest and one identifying a set of landmarks on a coronal whole-uterus balanced steady-state free precession scan, were trained on 167 and 71 fetal datasets across field strengths, acqui
Externí odkaz:
http://arxiv.org/abs/2408.06326
Autor:
West, Daniel J, Glang, Felix, Endres, Jonathan, Leitão, David, Zaiss, Moritz, Hajnal, Joseph V, Malik, Shaihan J
MRI systems are traditionally engineered to produce close to idealized performance, enabling a simplified pulse sequence design philosophy. An example of this is control of eddy currents produced by gradient fields; usually these are compensated by p
Externí odkaz:
http://arxiv.org/abs/2403.17575
Autor:
Dannecker, Maik, Kyriakopoulou, Vanessa, Cordero-Grande, Lucilio, Price, Anthony N., Hajnal, Joseph V., Rueckert, Daniel
We introduce a conditional implicit neural atlas (CINA) for spatio-temporal atlas generation from Magnetic Resonance Images (MRI) of the neurotypical and pathological fetal brain, that is fully independent of affine or non-rigid registration. During
Externí odkaz:
http://arxiv.org/abs/2403.08550
Autor:
Silva, Sara Neves, McElroy, Sarah, Verdera, Jordina Aviles, Colford, Kathleen, Clair, Kamilah St, Tomi-Tricot, Raphael, Uus, Alena, Ozenne, Valery, Hall, Megan, Story, Lisa, Pushparajah, Kuberan, Rutherford, Mary A, Hajnal, Joseph V, Hutter, Jana
Purpose: Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Methods: Deep learning-based detection of key brain landmarks on a whole-uterus EPI scan enables the subsequent fully a
Externí odkaz:
http://arxiv.org/abs/2401.10441
Autor:
Venturini, Lorenzo, Budd, Samuel, Farruggia, Alfonso, Wright, Robert, Matthew, Jacqueline, Day, Thomas G., Kainz, Bernhard, Razavi, Reza, Hajnal, Jo V.
The current approach to fetal anomaly screening is based on biometric measurements derived from individually selected ultrasound images. In this paper, we introduce a paradigm shift that attains human-level performance in biometric measurement by agg
Externí odkaz:
http://arxiv.org/abs/2401.01201
Autor:
Hajnal, Ákos
This paper presents a novel online learning method that aims at finding a separator hyperplane between data points labelled as either positive or negative. Since weights and biases of artificial neurons can directly be related to hyperplanes in high-
Externí odkaz:
http://arxiv.org/abs/2309.06049