Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
669-01-013 ISMRM Abstract

Differentiating MS and NMOSD from the Mimics: An Explainable Deep Learning Approach on 3D Brain FLAIR MRI

Primary: Neuro - Multiple Sclerosis
Secondary: Analysis Methods - Classification and Prediction
669-01-013 · Neuroinflammation: Metabolites, Function, and AI · Thursday, 14 May, 8:30 AM–9:25 AM · Digital Posters Row J
Keywords: Multiple Sclerosis Magnetic resonance imaging Deep learning Neuromyelitis optica spectrum disorder Deep learning interpretability
Accepted
Chunjie Guo 1, Ziqi Zhao2, Yinglu Sun2, shuting xu3, Feifei Deng1, Zhuo Wang1, Yan Wang2, Tao Jin3, Huimao Zhang1
1Department of Radiology, the First Hospital of Jilin University, Changchun, China
2College of Computer Science and Technology, Jilin University, Changchun, China
3Department of Neurology, the First Hospital of Jilin University, Changchun, China
Presenting Author: Chunjie Guo

Synopsis

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References

1. Wingerchuk, D. M. et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology 85, 177-189 (2015). https://doi.org/10.1212/WNL.0000000000001729 [doi]
2. Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. The Lancet. Neurology 17, 162-173, doi:10.1016/s1474-4422(17)30470-2 (2018). [doi]
3. Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks[C]//International conference on machine learning. PMLR, 2017: 3319-3328.
4. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
5. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4700-4708.
6. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, 2014.
7. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.

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http://echo.ismrm.org/p/ISMRM2026/669-01-013