15 citations · 22 across the 5 of their papers we have counts for
5 papers
Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images
Martynas Pocius, Wen Yan, Dean C. Barratt +4
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…
Semi-weakly-supervised neural network training for medical image registration
Yiwen Li, Yunguan Fu, Iani J. M. B. Gayo +11
For training registration networks, weak supervision from segmented corresponding regions-of-interest (ROIs) have been proven effective for (a) supplementing unsupervised methods,…
Machine Learning Force Fields with Data Cost Aware Training
Alexander Bukharin, Tianyi Liu, Shengjie Wang +4
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research.…
Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion
Shaheer U. Saeed, Tom Syer, Wen Yan +6
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-paramet…
Cross-Modality Image Registration using a Training-Time Privileged Third Modality
Qianye Yang, David Atkinson, Yunguan Fu +7
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 additi…