Publications (6)
Vector Representations of Vessel Trees
James Batten, Michiel Schaap, Matthew Sinclair +2
We introduce a novel framework for learning vector representations of tree-structured geometric data focusing on 3D vascular networks. Our approach employs two sequentially trained…
Image To Tree with Recursive Prompting
James Batten, Matthew Sinclair, Ben Glocker +1
Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typi…
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
Matthew Baugh, Jeremy Tan, Johanna P. Müller +3
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in…
Atlas-ISTN: Joint Segmentation, Registration and Atlas Construction with Image-and-Spatial Transformer Networks
Matthew Sinclair, Andreas Schuh, Karl Hahn +5
Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and do not guarantee a…
Detecting Outliers with Foreign Patch Interpolation
Jeremy Tan, Benjamin Hou, James Batten +2
In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the featur…
Zero-Shot Anomaly Detection with Pre-trained Segmentation Models
Matthew Baugh, James Batten, Johanna P. Müller +1
This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the performance of the WINCLIP f…