1.7k citations · 2k across the 66 of their papers we have counts for
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ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning
Yizhao Jin, Greg Slabaugh, Simon Lucas
Deep Reinforcement Learning (DRL) agents frequently face challenges in adapting to tasks outside their training distribution, including issues with over-fitting, catastrophic forge…
MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images
Amaya Gallagher-Syed, Luca Rossi, Felice Rivellese +4
Whole Slide Images (WSIs) present a challenging computer vision task due to their gigapixel size and presence of numerous artefacts. Yet they are a valuable resource for patient di…
Automated segmentation of rheumatoid arthritis immunohistochemistry stained synovial tissue
Amaya Gallagher-Syed, Abbas Khan, Felice Rivellese +4
Rheumatoid Arthritis (RA) is a chronic, autoimmune disease which primarily affects the joint's synovial tissue. It is a highly heterogeneous disease, with wide cellular and molecul…
Joint Dense-Point Representation for Contour-Aware Graph Segmentation
Kit Mills Bransby, Greg Slabaugh, Christos Bourantas +1
We present a novel methodology that combines graph and dense segmentation techniques by jointly learning both point and pixel contour representations, thereby leveraging the benefi…
Graph Neural Networks in Vision-Language Image Understanding: A Survey
Henry Senior, Gregory Slabaugh, Shanxin Yuan +1
2D image understanding is a complex problem within computer vision, but it holds the key to providing human-level scene comprehension. It goes further than identifying the objects…
3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph Convolutional Networks
Kit Mills Bransby, Vincenzo Tufaro, Murat Cap +3
X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity. However, XCA images are 2D and there…