Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Saeed, Shaheer U"'
Autor:
Chen, Yaxi, Ivanova, Aleksandra, Saeed, Shaheer U., Hargunani, Rikin, Huang, Jie, Liu, Chaozong, Hu, Yipeng
The recently proposed Segment Anything Model (SAM) is a general tool for image segmentation, but it requires additional adaptation and careful fine-tuning for medical image segmentation, especially for small, irregularly-shaped, and boundary-ambiguou
Externí odkaz:
http://arxiv.org/abs/2407.17933
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:
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
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
Autor:
Yi, Weixi, Stavrinides, Vasilis, Baum, Zachary M. C., Yang, Qianye, Barratt, Dean C., Clarkson, Matthew J., Hu, Yipeng, Saeed, Shaheer U.
We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. Thi
Externí odkaz:
http://arxiv.org/abs/2308.11376
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., Syer, Tom, Yan, Wen, Yang, Qianye, Emberton, Mark, Punwani, Shonit, Clarkson, Matthew J., Barratt, Dean C., Hu, Yipeng
We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text, to control lesion presence and sequence, as well as to generate paired bi-parametric images conditioned on images e.g. for generating diffusion-weighte
Externí odkaz:
http://arxiv.org/abs/2303.02094
Autor:
Min, Zhe, Baum, Zachary M. C., Saeed, Shaheer U., Emberton, Mark, Barratt, Dean C., Taylor, Zeike A., Hu, Yipeng
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world c
Externí odkaz:
http://arxiv.org/abs/2302.10343