Zobrazeno 1 - 10
of 5 227
pro vyhledávání: '"Barratt, P."'
Autor:
Yi, Weixi, Wang, Yipei, Thorley, Natasha, Ng, Alexander, Punwani, Shonit, Kasivisvanathan, Veeru, Barratt, Dean C., Saeed, Shaheer Ullah, Hu, Yipeng
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater accuracy improvement. However, measuring diffusion patterns in DWI se
Externí odkaz:
http://arxiv.org/abs/2411.07416
Autor:
Huang, Shiqi, Xu, Tingfa, Shen, Ziyi, Saeed, Shaheer Ullah, Yan, Wen, Barratt, Dean, Hu, Yipeng
This paper describes a new spatial correspondence representation based on paired regions-of-interest (ROIs), for medical image registration. The distinct properties of the proposed ROI-based correspondence are discussed, in the context of potential b
Externí odkaz:
http://arxiv.org/abs/2410.14083
Autor:
Barratt, Luke A., Aston, John A. D.
When it came to Covid-19, timing was everything. This paper considers the spatiotemporal dynamics of the Covid-19 pandemic via a developed methodology of non-Euclidean spatially aware functional registration. In particular, the daily SARS-CoV-2 incid
Externí odkaz:
http://arxiv.org/abs/2407.17132
Autor:
Xu, Yinsong, Wang, Yipei, Shen, Ziyi, Gayo, Iani J. M. B., Thorley, Natasha, Punwani, Shonit, Men, Aidong, Barratt, Dean, Chen, Qingchao, Hu, Yipeng
The Gleason groups serve as the primary histological grading system for prostate cancer, providing crucial insights into the cancer's potential for growth and metastasis. In clinical practice, pathologists determine the Gleason groups based on specim
Externí odkaz:
http://arxiv.org/abs/2407.05796
Autor:
Li, Qi, Shen, Ziyi, Yang, Qianye, Barratt, Dean C., Clarkson, Matthew J., Vercauteren, Tom, Hu, Yipeng
Reconstructing 2D freehand Ultrasound (US) frames into 3D space without using a tracker has recently seen advances with deep learning. Predicting good frame-to-frame rigid transformations is often accepted as the learning objective, especially when t
Externí odkaz:
http://arxiv.org/abs/2407.05767
Autor:
Min, Zhe, Baum, Zachary M. C., Saeed, Shaheer U., Emberton, Mark, Barratt, Dean C., Taylor, Zeike A., Hu, Yipeng
This paper investigates both biomechanical-constrained non-rigid medical image registrations and accurate identifications of material properties for soft tissues, using physics-informed neural networks (PINNs). The complex nonlinear elasticity theory
Externí odkaz:
http://arxiv.org/abs/2407.03292
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:
Huang, Shiqi, Xu, Tingfa, Shen, Ziyi, Saeed, Shaheer Ullah, Yan, Wen, Barratt, Dean, Hu, Yipeng
The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformations (e.g., rigid, affine, and splines). Rethinking the existing paradig
Externí odkaz:
http://arxiv.org/abs/2405.10879
Autor:
Pocius, Martynas, Yan, Wen, Barratt, Dean C., Emberton, Mark, Clarkson, Matthew J., Hu, Yipeng, Saeed, Shaheer U.
In this paper we propose a reinforcement learning based weakly supervised system for localisation. We train a controller function to localise regions of interest within an image by introducing a novel reward definition that utilises non-binarised cla
Externí odkaz:
http://arxiv.org/abs/2402.13778
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