Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
401-02-001 ISMRM Abstract

TumorCLIP: Lightweight Vision–Language Fusion for Explainable MRI-Based Brain Tumor Classification

Primary: Analysis Methods - Classification and Prediction
Secondary: Neuro - Tumors
401-02-001 · Foundation Models · Tuesday, 12 May, 8:20 AM–10:10 AM · Hall 1A
Keywords: MRI Multimodal imaging Brain Tumor classification Vision-language model
Accepted
Yishaoying Jia1, Jinfu Niu2, Zongyu Li 2, Jia Guo2,3
1Digital Media Technology, Xi'an Jiaotong-Liverpool University, China
2Biomedical Engineering, Columbia University, New York, United States of America
3Psychiatry, Columbia University, New York, United States of America
Presenting Author: Zongyu Li

Synopsis

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References

1. Dorfner FJ, Patel JB, Kalpathy-Cramer J, et al. A review of deep learning for brain tumor analysis in MRI. NPJ Precision Oncology. 2025;9:2. https://doi.org/10.1038/s41698-024-00789-2. [doi]
2. Cui C, Yang H, Wang Y, et al. Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review. Progress in Biomedical Engineering (Bristol). 2023;5(2):10.1088/2516-1091/acc2fe. https://doi.org/10.1088/2516-1091/acc2fe. [doi]
3. Fernando2rad. Brain Tumor MRI Images (17 Classes). Kaggle. https://www.kaggle.com/datasets/fernando2rad/brain-tumor-mri-images-17-classes. Accessed August 20, 2025
4. Wang P, Yang Q, He Z, Yuan Y. Vision transformers in multi-modal brain tumor MRI segmentation: a review. Medical Radiology and Imaging. 2023;2(1):100004. https://doi.org/10.1016/j.metrad.2023.100004 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/401-02-001