51 citations · 138 across the 42 of their papers we have counts for
8 papers · 1 filter
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Weixi Yi, Yipei Wang, Natasha Thorley +6
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater…
SAMReg: SAM-enabled Image Registration with ROI-based Correspondence
Shiqi Huang, Tingfa Xu, Ziyi Shen +4
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…
Segmentation by registration-enabled SAM prompt engineering using five reference images
Yaxi Chen, Aleksandra Ivanova, Shaheer U. Saeed +4
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 segment…
Biomechanics-informed Non-rigid Medical Image Registration and its Inverse Material Property Estimation with Linear and Nonlinear Elasticity
Zhe Min, Zachary M. C. Baum, Shaheer U. Saeed +4
This paper investigates both biomechanical-constrained non-rigid medical image registrations and accurate identifications of material properties for soft tissues, using physics-inf…
Competing for pixels: a self-play algorithm for weakly-supervised segmentation
Shaheer U. Saeed, Shiqi Huang, João Ramalhinho +8
Weakly-supervised segmentation (WSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs),…
One registration is worth two segmentations
Shiqi Huang, Tingfa Xu, Ziyi Shen +4
The goal of image registration is to establish spatial correspondence between two or more images, traditionally through dense displacement fields (DDFs) or parametric transformatio…
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,…