Imperial College London
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DrPaulBentley

Faculty of Medicine, Department of Brain Sciences

Senior Clinical Research Fellow
 
 
 
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Contact

 

p.bentley

 
 
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Location

 

10L21Charing Cross HospitalCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Gasimova:2020:10.1007/978-3-030-59728-3_33,
author = {Gasimova, A and Seegoolam, G and Chen, L and Bentley, P and Rueckert, D},
doi = {10.1007/978-3-030-59728-3_33},
pages = {333--342},
title = {Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation},
url = {http://dx.doi.org/10.1007/978-3-030-59728-3_33},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In light of recent works exploring automated pathological diagnosis, studies have also shown that medical text reports can be generated with varying levels of efficacy. Brain diffusion-weighted MRI (DWI) has been used for the diagnosis of ischaemia in which brain death can follow in immediate hours. It is therefore of the utmost importance to obtain ischaemic brain diagnosis as soon as possible in a clinical setting. Previous studies have shown that MRI acquisition can be accelerated using variable-density Cartesian undersampling methods. In this study, we propose an accelerated DWI acquisition pipeline for the purpose of generating text reports containing diagnostic information. We demonstrate that we can learn a semantic-preserving latent space for minor as well as extremely undersampled MR images capable of achieving promising results on a diagnostic report generation task.
AU  - Gasimova,A
AU  - Seegoolam,G
AU  - Chen,L
AU  - Bentley,P
AU  - Rueckert,D
DO  - 10.1007/978-3-030-59728-3_33
EP  - 342
PY  - 2020///
SN  - 0302-9743
SP  - 333
TI  - Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation
UR  - http://dx.doi.org/10.1007/978-3-030-59728-3_33
ER  -
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