Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
331-02-005 / 331-02-005 ISMRM Abstract

Coarse-to-Fine Meta-Reweighting with Dynamic Retrieval for Pediatric Brain Tumor Segmentation

Primary: Analysis Methods - Segmentation and Detection
Secondary: Pediatrics - Cancer
331-02-005 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace
331-02-005 · AI Frontiers in Image Analysis · Monday, 11 May, 1:50 PM–3:06 PM · Roof Terrace
Keywords: Brain Tumor Segmentation BraTS-PEDs Data Scarcity Meta-Learning Loss Reweighting
Accepted
Abdulkhalek Al-Fakih1,2, Kanghyun Ryu3, Mohammed Al-masni 1
1Department of Artificial Intelligence and Data Science, Sejong University, Seoul, Korea, Republic of
2Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of
3Korea Institute of Science and Technology, Korea Institute of Science and Technology (KIST), Seould, Korea, Republic of
Presenting Author: Mohammed Al-masni

Synopsis

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References

1. Kirscher T, Faisan S, Coubez X, Barrier L, Meyer P. PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention). Springer (2025). https://doi.org/10.1007/978-3-032-04981-0_45 [doi]
2. Li J, et al. A systematic collection of medical image datasets for deep learning. ACM Computing Surveys 56, 1-51 (2023).
3. Wang H, Jin Q, Li S, Liu S, Wang M, Song Z. A comprehensive survey on deep active learning in medical image analysis. Medical Image Analysis 95, 103201 (2024). https://doi.org/10.1145/3615862 [doi]
4. Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani S. Can deep adult lung segmentation models generalize to the pediatric population? Expert systems with applications 229, 120531 (2023). https://doi.org/10.1016/j.eswa.2023.120531 [doi]
5. Hatamizadeh A, et al. Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision) (2022). DOI: 10.1109/WACV51458.2022.00181 [doi]
6. Kazerooni AF, et al. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023. arXiv preprint arXiv:240708855 abs/2407.08855, (2024). https://doi.org/10.48550/arXiv.2407.08855 [doi]
7. Hacohen G, Weinshall D. On the power of curriculum learning in training deep networks. In: International conference on machine learning). PMLR (2019). https://doi.org/10.48550/arXiv.1904.03626 [doi]
8. Shu J, et al. Meta-weight-net: Learning an explicit mapping for sample weighting. Advances in neural information processing systems 32, (2019). https://doi.org/10.48550/arXiv.1902.07379 [doi]
9. Mirikharaji Z, Yan Y, Hamarneh G. Learning to segment skin lesions from noisy annotations. In: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings 1). Springer (2019). https://doi.org/10.1007/978-3-030-33391-1 [doi]

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http://echo.ismrm.org/p/ISMRM2026/331-02-005