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Found 9 papers, 2 papers with code
Predicting Spine Geometry and Scoliosis from DXA Scans
Our objective in this paper is to estimate spine curvature in DXA scans.
Contouring by Unit Vector Field Regression
This work introduces a simple deep-learning based method to delineate contours by `walking' along learnt unit vector fields.
Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime
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.
Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading
(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.
SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans
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.
Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging
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.
A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy.
A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs.
Self-Supervised Learning for Spinal MRIs
We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.
