Zobrazeno 1 - 10
of 94
pro vyhledávání: '"Bonmati, Ester"'
Autor:
Wu, Xiangcen, Wang, Yipei, Yang, Qianye, Thorley, Natasha, Punwani, Shonit, Kasivisvanathan, Veeru, Bonmati, Ester, Hu, Yipeng
Prostate cancer diagnosis through MR imaging have currently relied on radiologists' interpretation, whilst modern AI-based methods have been developed to detect clinically significant cancers independent of radiologists. In this study, we propose to
Externí odkaz:
http://arxiv.org/abs/2410.23084
Autor:
Saeed, Shaheer U., Huang, Shiqi, Ramalhinho, João, Gayo, Iani J. M. B., Montaña-Brown, Nina, Bonmati, Ester, Pereira, Stephen P., Davidson, Brian, Barratt, Dean C., Clarkson, Matthew J., Hu, Yipeng
Weakly-supervised segmentation (WSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs), posing a significant challenge. Despite this, WSS methods have attract
Externí odkaz:
http://arxiv.org/abs/2405.16628
Autor:
Saeed, Shaheer U., Ramalhinho, João, Pinnock, Mark, Shen, Ziyi, Fu, Yunguan, Montaña-Brown, Nina, Bonmati, Ester, Barratt, Dean C., Pereira, Stephen P., Davidson, Brian, Clarkson, Matthew J., Hu, Yipeng
Supervised machine learning-based medical image computing applications necessitate expert label curation, while unlabelled image data might be relatively abundant. Active learning methods aim to prioritise a subset of available image data for expert
Externí odkaz:
http://arxiv.org/abs/2212.01703
Autor:
Bonmati, Ester, Hu, Yipeng, Grimwood, Alexander, Johnson, Gavin J., Goodchild, George, Keane, Margaret G., Gurusamy, Kurinchi, Davidson, Brian, Clarkson, Matthew J., Pereira, Stephen P., Barratt, Dean C.
Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a
Externí odkaz:
http://arxiv.org/abs/2110.06367
Autor:
Saeed, Shaheer U., Ramalhinho, João, Pinnock, Mark, Shen, Ziyi, Fu, Yunguan, Montaña-Brown, Nina, Bonmati, Ester, Barratt, Dean C., Pereira, Stephen P., Davidson, Brian, Clarkson, Matthew J., Hu, Yipeng
Publikováno v:
In Medical Image Analysis July 2024 95
Autor:
Fu, Yunguan, Brown, Nina Montaña, Saeed, Shaheer U., Casamitjana, Adrià, Baum, Zachary M. C., Delaunay, Rémi, Yang, Qianye, Grimwood, Alexander, Min, Zhe, Blumberg, Stefano B., Iglesias, Juan Eugenio, Barratt, Dean C., Bonmati, Ester, Alexander, Daniel C., Clarkson, Matthew J., Vercauteren, Tom, Hu, Yipeng
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
Comment: Accepted in The Journal of Open Source Software (JOSS)
Comment: Accepted in The Journal of Open Source Software (JOSS)
Externí odkaz:
http://arxiv.org/abs/2011.02580
Effective transperineal ultrasound image guidance in prostate external beam radiotherapy requires consistent alignment between probe and prostate at each session during patient set-up. Probe placement and ultrasound image inter-pretation are manual t
Externí odkaz:
http://arxiv.org/abs/2010.02732
Autor:
Baum, Zachary M C, Bonmati, Ester, Cristoni, Lorenzo, Walden, Andrew, Prados, Ferran, Kanber, Baris, Barratt, Dean C, Hawkes, David J, Parker, Geoffrey J M, Wheeler-Kingshott, Claudia A M Gandini, Hu, Yipeng
We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system c
Externí odkaz:
http://arxiv.org/abs/2008.08840
Autor:
Hu, Yipeng, Modat, Marc, Gibson, Eli, Li, Wenqi, Ghavami, Nooshin, Bonmati, Ester, Wang, Guotai, Bandula, Steven, Moore, Caroline M., Emberton, Mark, Ourselin, Sébastien, Noble, J. Alison, Barratt, Dean C., Vercauteren, Tom
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspo
Externí odkaz:
http://arxiv.org/abs/1807.03361
Autor:
Hu, Yipeng, Gibson, Eli, Ghavami, Nooshin, Bonmati, Ester, Moore, Caroline M., Emberton, Mark, Vercauteren, Tom, Noble, J. Alison, Barratt, Dean C.
We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer
Externí odkaz:
http://arxiv.org/abs/1805.10665