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20172026
most citedSelf-Supervised Learning for Spinal MRIs

15 citations · 27 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores

Maria Monzon, Andrew Zisserman, Robin Y. Park +2

Lumbar spine degeneration is a major contributor to chronic low back pain and is routinely assessed on MRI using ordinal grading systems, e.g. normal, mild, moderate, severe. Conse…

cs.CV2025

UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentation

Emmanuelle Bourigault, Amir Jamaludin, Abdullah Hamdi

In medical imaging, the primary challenge is collecting large-scale labeled data due to privacy concerns, logistics, and high labeling costs. In this work, we present the UK Bioban…

cs.CV2023

Predicting Spine Geometry and Scoliosis from DXA Scans

Amir Jamaludin, Timor Kadir, Emma Clark +1

Our objective in this paper is to estimate spine curvature in DXA scans. To this end we first train a neural network to predict the middle spine curve in the scan, and then use an…

cs.CV2021

Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging

Rhydian Windsor, Amir Jamaludin, Timor Kadir +1

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. Specifically, we use a large…

cs.CV2020

The Ladder Algorithm: Finding Repetitive Structures in Medical Images by Induction

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. We the…

cs.CV201715 cited

Self-Supervised Learning for Spinal MRIs

Amir Jamaludin, Timor Kadir, Andrew Zisserman

A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' sel…