Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Shaheer U. Saeed"'
Autor:
João Ramalhinho, Bongjin Koo, Nina Montaña-Brown, Shaheer U. Saeed, Ester Bonmati, Kurinchi Gurusamy, Stephen P. Pereira, Brian Davidson, Yipeng Hu, Matthew J. Clarkson
Publikováno v:
International Journal of Computer Assisted Radiology and Surgery. 17:1461-1468
Purpose The registration of Laparoscopic Ultrasound (LUS) to CT can enhance the safety of laparoscopic liver surgery by providing the surgeon with awareness on the relative positioning between critical vessels and a tumour. In an effort to provide a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d3ec4d6e220030fa2fee3ba6a900a25
https://doi.org/10.1007/s11548-022-02605-3
https://doi.org/10.1007/s11548-022-02605-3
Autor:
Shaheer U. Saeed, Yunguan Fu, Vasilis Stavrinides, Zachary M.C. Baum, Qianye Yang, Mirabela Rusu, Richard E. Fan, Geoffrey A. Sonn, J. Alison Noble, Dean C. Barratt, Yipeng Hu
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88213b98f68ee45fe8d5bb157b338e25
Publikováno v:
Cancer Prevention Through Early Detection ISBN: 9783031179785
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b69645e81e7f891916737bfeba635f52
https://doi.org/10.1007/978-3-031-17979-2_15
https://doi.org/10.1007/978-3-031-17979-2_15
Autor:
Yipeng Hu, Qianye Yang, J. Alison Noble, Dean C. Barratt, Zachary M. C. Baum, Geoffrey A. Sonn, Vasilis Stavrinides, Richard E. Fan, Shaheer U. Saeed, Mirabela Rusu, Yunguan Fu
Publikováno v:
Simplifying Medical Ultrasound ISBN: 9783030875824
ASMUS@MICCAI
ASMUS@MICCAI
The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13b44420f69a2a45fd495ab391b575a4
https://doi.org/10.1007/978-3-030-87583-1_19
https://doi.org/10.1007/978-3-030-87583-1_19
Autor:
Qianye Yang, Liam F. Chalcroft, Andre Altmann, Giulio V. Minore, Sophie A. Martin, Yipeng Hu, Zachary M. C. Baum, Jiongqi Qu, Imraj R. D. Singh, Shaheer U. Saeed, Iani J. M. B. Gayo
Publikováno v:
Simplifying Medical Ultrasound ISBN: 9783030875824
ASMUS@MICCAI
ASMUS@MICCAI
When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in grou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9142cd211cf7a438c5343f018704d88b
https://doi.org/10.1007/978-3-030-87583-1_3
https://doi.org/10.1007/978-3-030-87583-1_3
Autor:
Yipeng Hu, Mark Emberton, Dean C. Barratt, Mark A. Pinnock, Zeike A. Taylor, Shaheer U. Saeed
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597184
MICCAI (4)
MICCAI (4)
In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1402059f188b4b01c36ff34134d6b0c6
https://doi.org/10.1007/978-3-030-59719-1_63
https://doi.org/10.1007/978-3-030-59719-1_63
Autor:
Yipeng Hu, Shaheer U. Saeed, Stefano B. Blumberg, Qianye Yang, Juan Eugenio Iglesias, Zhe Min, Dean C. Barratt, Zachary M. C. Baum, Adrià Casamitjana, Ester Bonmati, Yunguan Fu, Nina Montaña Brown, Alexander Grimwood, Rémi Delaunay, Tom Vercauteren, Daniel C. Alexander, Matthew J. Clarkson
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b73e715c4028d3903412c111afc282dd