20 citations · 49 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
Weixi Yi, Vasilis Stavrinides, Zachary M. C. Baum +5
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 probl…
Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction
Qi Li, Ziyi Shen, Qian Li +5
Three-dimensional (3D) freehand ultrasound (US) reconstruction without using any additional external tracking device has seen recent advances with deep neural networks (DNNs). In t…
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…
Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration
Zachary MC Baum, Yipeng Hu, Dean C Barratt
Neural networks have been proposed for medical image registration by learning, with a substantial amount of training data, the optimal transformations between image pairs. These tr…
Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data
Zachary MC Baum, Tamas Ungi, Christopher Schlenger +2
Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on p…