561-02-009 ISMRM Abstract

Automatic MRI contrast classification combining metadata and interpretable image-based features

Primary: Analysis Methods - Classification and Prediction

Secondary: Analysis Methods - Data Processing

561-02-009 · Classification and Analysis in the Body · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row B

Keywords: Classification Interpretable AI MRI contrast Metadata Image-based features

Accepted

Marc Saghiah^1,2, Michel Dojat^1,2, Sophie Ancelet³, Benjamin Lemasson ¹

¹Grenoble Institut de Neurosciences, Grenoble, France

²Inria (Grenoble), France

³PSE-SANTE-SESANE/LEPID, ASNR - Autorité de sureté nucléaire et de radioprotection, Fontenay-aux-Roses, France

Presenting Author: Benjamin Lemasson

Synopsis

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References

1. Cluceru J, Lupo JM, Interian Y, Bove R, Crane JC. Improving the Automatic Classification of Brain MRI Acquisition Contrast with Machine Learning. J Digit Imaging. 2023 Feb;36(1):289-305. DOI: 10.1007/s10278-022-00690-z [doi]

2. Baumgärtner GL, Hamm CA, Schulze-Weddige S, Ruppel R, Beetz NL, Rudolph M, Dräger F, Froböse KP, Posch H, Lenk J, Biessmann F, Penzkofer T. Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI. Eur J Radiol. 2023 Sep;166:110964. DOI: 10.1016/j.ejrad.2023.110964 [doi]

3. https://anr.fr/Projet-ANR-21-CE45-0038

4. Sadri AR, Janowczyk A, Zhou R, Verma R, Beig N, Antunes J, Madabhushi A, Tiwari P, Viswanath SE. Technical Note: MRQy - An open-source tool for quality control of MR imaging data. Med Phys. 2020 Dec;47(12):6029-6038. DOI: 10.1002/mp.14593 [doi]

5. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI. From Local Explanations to Global Understanding with Explainable AI for Trees. Nat Mach Intell. 2020 Jan;2(1):56-67. DOI: 10.1038/s42256-019-0138-9 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-02-009