Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Matthew, Jacqueline"'
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:
Iskandar, Michelle, Mannering, Harvey, Sun, Zhanxiang, Matthew, Jacqueline, Kerdegari, Hamideh, Peralta, Laura, Xochicale, Miguel
Prenatal ultrasound imaging is the first-choice modality to assess fetal health. Medical image datasets for AI and ML methods must be diverse (i.e. diagnoses, diseases, pathologies, scanners, demographics, etc), however there are few public ultrasoun
Externí odkaz:
http://arxiv.org/abs/2304.03941
Autor:
Zimmer, Veronika A., Gomez, Alberto, Skelton, Emily, Wright, Robert, Wheeler, Gavin, Deng, Shujie, Ghavami, Nooshin, Lloyd, Karen, Matthew, Jacqueline, Kainz, Bernhard, Rueckert, Daniel, Hajnal, Joseph V., Schnabel, Julia A.
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-
Externí odkaz:
http://arxiv.org/abs/2206.14746
Autor:
Bautista, Thea, Matthew, Jacqueline, Kerdegari, Hamideh, Pereira, Laura Peralta, Xochicale, Miguel
In this work, we present an empirical study of DCGANs, including hyperparameter heuristics and image quality assessment, as a way to address the scarcity of datasets to investigate fetal head ultrasound. We present experiments to show the impact of d
Externí odkaz:
http://arxiv.org/abs/2206.01731
Autor:
Budd, Samuel, Day, Thomas, Simpson, John, Lloyd, Karen, Matthew, Jacqueline, Skelton, Emily, Razavi, Reza, Kainz, Bernhard
Probably yes. -- Supervised Deep Learning dominates performance scores for many computer vision tasks and defines the state-of-the-art. However, medical image analysis lags behind natural image applications. One of the many reasons is the lack of wel
Externí odkaz:
http://arxiv.org/abs/2107.14682
Autor:
Chotzoglou, Elisa, Day, Thomas, Tan, Jeremy, Matthew, Jacqueline, Lloyd, David, Razavi, Reza, Simpson, John, Kainz, Bernhard
Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed
Externí odkaz:
http://arxiv.org/abs/2012.03679
Autor:
Meng, Qingjie, Matthew, Jacqueline, Zimmer, Veronika A., Gomez, Alberto, Lloyd, David F. A., Rueckert, Daniel, Kainz, Bernhard
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an intere
Externí odkaz:
http://arxiv.org/abs/2011.00739
Autor:
Treivase, Simona, Gomez, Alberto, Matthew, Jacqueline, Skelton, Emily, Schnabel, Julia A., Toussaint, Nicolas
Ultrasound (US) imaging is one of the most commonly used non-invasive imaging techniques. However, US image acquisition requires simultaneous guidance of the transducer and interpretation of images, which is a highly challenging task that requires ye
Externí odkaz:
http://arxiv.org/abs/2007.06272
Autor:
Magnetti, Cesare, Zimmer, Veronika, Ghavami, Nooshin, Skelton, Emily, Matthew, Jacqueline, Lloyd, Karen, Hajnal, Jo, Schnabel, Julia A., Gomez, Alberto
We present a computational method for real-time, patient-specific simulation of 2D ultrasound (US) images. The method uses a large number of tracked ultrasound images to learn a function that maps position and orientation of the transducer to ultraso
Externí odkaz:
http://arxiv.org/abs/2005.04931
Autor:
Wright, Robert, Gomez, Alberto, Zimmer, Veronika A., Toussaint, Nicolas, Khanal, Bishesh, Matthew, Jacqueline, Skelton, Emily, Kainz, Bernhard, Rueckert, Daniel, Hajnal, Joseph V., Schnabel, Julia A.
Publikováno v:
In Medical Image Analysis October 2023 89
