Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Fu, Yunguan"'
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani J. M. B., Yang, Qianye, Min, Zhe, Saeed, Shaheer U., Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Barratt, Dean C., Prisacariu, Victor A., Hu, Yipeng
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods, and (b) being used independently in registration tasks in which unsupe
Externí odkaz:
http://arxiv.org/abs/2402.10728
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Denoising diffusion models have found applications in image segmentation by generating segmented masks conditioned on images. Existing studies predominantly focus on adjusting model architecture or improving inference, such as test-time sampling stra
Externí odkaz:
http://arxiv.org/abs/2308.16355
Graph neural networks (GNNs) have been proposed for medical image segmentation, by predicting anatomical structures represented by graphs of vertices and edges. One such type of graph is predefined with fixed size and connectivity to represent a refe
Externí odkaz:
http://arxiv.org/abs/2303.06550
Recently, denoising diffusion probabilistic models (DDPM) have been applied to image segmentation by generating segmentation masks conditioned on images, while the applications were mainly limited to 2D networks without exploiting potential benefits
Externí odkaz:
http://arxiv.org/abs/2303.06040
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:
Cardoso, M. Jorge, Li, Wenqi, Brown, Richard, Ma, Nic, Kerfoot, Eric, Wang, Yiheng, Murrey, Benjamin, Myronenko, Andriy, Zhao, Can, Yang, Dong, Nath, Vishwesh, He, Yufan, Xu, Ziyue, Hatamizadeh, Ali, Zhu, Wentao, Liu, Yun, Zheng, Mingxin, Tang, Yucheng, Yang, Isaac, Zephyr, Michael, Hashemian, Behrooz, Alle, Sachidanand, Darestani, Mohammad Zalbagi, Budd, Charlie, Modat, Marc, Vercauteren, Tom, Wang, Guotai, Li, Yiwen, Hu, Yipeng, Fu, Yunguan, Gorman, Benjamin, Johnson, Hans, Genereaux, Brad, Erdal, Barbaros S., Gupta, Vikash, Diaz-Pinto, Andres, Dourson, Andre, Maier-Hein, Lena, Jaeger, Paul F., Baumgartner, Michael, Kalpathy-Cramer, Jayashree, Flores, Mona, Kirby, Justin, Cooper, Lee A. D., Roth, Holger R., Xu, Daguang, Bericat, David, Floca, Ralf, Zhou, S. Kevin, Shuaib, Haris, Farahani, Keyvan, Maier-Hein, Klaus H., Aylward, Stephen, Dogra, Prerna, Ourselin, Sebastien, Feng, Andrew
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be use
Externí odkaz:
http://arxiv.org/abs/2211.02701
Autor:
Li, Yiwen, Fu, Yunguan, Gayo, Iani, Yang, Qianye, Min, Zhe, Saeed, Shaheer, Yan, Wen, Wang, Yipei, Noble, J. Alison, Emberton, Mark, Clarkson, Matthew J., Huisman, Henkjan, Barratt, Dean, Prisacariu, Victor Adrian, Hu, Yipeng
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and
Externí odkaz:
http://arxiv.org/abs/2209.05160
Autor:
Yang, Qianye, Atkinson, David, Fu, Yunguan, Syer, Tom, Yan, Wen, Punwani, Shonit, Clarkson, Matthew J., Barratt, Dean C., Vercauteren, Tom, Hu, Yipeng
In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an examp
Externí odkaz:
http://arxiv.org/abs/2207.12901
Autor:
Saeed, Shaheer U., Fu, Yunguan, Stavrinides, Vasilis, Baum, Zachary M. C., Yang, Qianye, Rusu, Mirabela, Fan, Richard E., Sonn, Geoffrey A., Noble, J. Alison, Barratt, Dean C., Hu, Yipeng
Publikováno v:
Medical Image Analysis, Volume 78, 2022, 102427, ISSN 1361-8415
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:
http://arxiv.org/abs/2203.14258
Autor:
Saeed, Shaheer U., Yan, Wen, Fu, Yunguan, Giganti, Francesco, Yang, Qianye, Baum, Zachary M. C., Rusu, Mirabela, Fan, Richard E., Sonn, Geoffrey A., Emberton, Mark, Barratt, Dean C., Hu, Yipeng
Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific target tasks, also named as task amenability, is needed. A task-speci
Externí odkaz:
http://arxiv.org/abs/2202.09798