561-02-007 ISMRM Abstract

Impact of MR task aware data pre-processing and feature collation for robust Foundation model-based performance

Primary: Analysis Methods - Foundation Models

Secondary: Analysis Methods - Classification and Prediction

561-02-007 · Classification and Analysis in the Body · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row B

Keywords: Analysis/Processing Contrast-agnostic AI Foundation Model

Accepted

Deepa Anand¹, Gurunath Reddy Madhumani ¹, Seyed Iman Zare Estakhraji², Vanika Singhal¹, Sandeep S Kaushik², Zhijian Yang², Marc Lebel², Erhan Bas², Chitresh Bhushan³, Uday Patil¹, Dattesh Dayanand Shanbhag¹

¹GE HealthCare, Bengaluru, India

²GE HealthCare, San Ramon, United States of America

³GE HealthCare., NY, United States of America

Presenting Author: Gurunath Reddy Madhumani

Synopsis

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References

1. https://doi.org/10.48550/arXiv.2304.07193 [doi]

2. https://doi.org/10.48550/arXiv.2410.09759 [doi]

3. https://doi.org/10.48550/arXiv.2509.21249 [doi]

4. Bhushan, C., Singhal, V., & Shanbhag, D.D. Contrast neutralization as a strategy to achieve generalizability in MR deep learning applications. Proceedings of the ISMRM 2024, Singapore, Abstract #1196.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-02-007