no code implementations • 26 May 2023 • Amir Jamaludin, Sarim Ather, Timor Kadir, Rhydian Windsor
This work introduces a simple deep-learning based method to delineate contours by `walking' along learnt unit vector fields.
no code implementations • 30 Mar 2023 • Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
This paper explores training medical vision-language models (VLMs) -- where the visual and language inputs are embedded into a common space -- with a particular focus on scenarios where training data is limited, as is often the case in clinical datasets.
no code implementations • 27 Jun 2022 • Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
(iii) We also apply SCT to an existing problem: radiological grading of inter-vertebral discs (IVDs) in lumbar MR scans for common degenerative changes. We show that by considering the context of vertebral bodies in the image, SCT improves the accuracy for several gradings compared to previously published model.
no code implementations • 3 May 2022 • Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
This technical report presents SpineNetV2, an automated tool which: (i) detects and labels vertebral bodies in clinical spinal magnetic resonance (MR) scans across a range of commonly used sequences; and (ii) performs radiological grading of lumbar intervertebral discs in T2-weighted scans for a range of common degenerative changes.
1 code implementation • 14 Jul 2021 • Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
This paper explores the use of self-supervised deep learning in medical imaging in cases where two scan modalities are available for the same subject.
no code implementations • 6 Jul 2020 • Rhydian Windsor, Amir Jamaludin, Timor Kadir, Andrew Zisserman
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs.
no code implementations • 30 Jan 2020 • Rhydian Windsor, Amir Jamaludin
In this paper we introduce the Ladder Algorithm; a novel recurrent algorithm to detect repetitive structures in natural images with high accuracy using little training data.