562-03-003 ISMRM Abstract

Slice-wise Vision Transformer with Max-Aggregation for Multi-label Quality Assessment of Ultra-Low-Field Pediatric Brain MRI

Primary: Neuro - Head & Neck/ENT

Secondary: Physics & Engineering - Low-Field MRI

562-03-003 · Machine Learning in Neuroimaging · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row C

Accepted

Jiaqi Dou ¹, Song Tian²

¹Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine,, Tsinghua University, Beijing, China

²Philips Healthcare (Beijing), Beijing, China

Presenting Author: Jiaqi Dou

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References

1. Lepore, N., Linguraru, M. G., Deoni, S., Tanedo, J., Rouhi, R., Tapp, A., Nelson, M., Nankabirwa, V., Deoni, S., Donald, K., Parkar, S., Kaleem, S., & Osmani, S. (2025). Low field pediatric brain magnetic resonance Image Segmentation and quality Assurance (LISA). Medical Image Computing and Computer Assisted Intervention 2025 (MICCAI). Zenodo. https://doi.org/10.5281/zenodo.15081583 [doi]

2. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.

3. Lepore, N., & Linguraru, M. G. (2025). Low Field Pediatric Brain Magnetic Resonance Im-age Segmentation and Quality Assurance: First MICCAI Challenge, LISA 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings (p. 77). Springer Nature.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-03-003