Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation
Gasimova A, Seegoolam G, Chen L, Bentley P, Rueckert D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12267 LNCS
Pages Range: 333-342
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030597276
DOI: 10.1007/978-3-030-59728-3_33
Abstract
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.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Gasimova, A., Seegoolam, G., Chen, L., Bentley, P., & Rueckert, D. (2020). Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 333-342). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Gasimova, Aydan, et al. "Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation." Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, Springer Science and Business Media Deutschland GmbH, 2020. 333-342.
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