mlsanityData to 7 Oct 2026

The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv 2404.15009 ↗First posted 23 Apr 2024cs.CV eess.IVcited by 13

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Abstract (arXiv)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 3 of its 32 references that are in the map. First posted on arXiv 23 Apr 2024.

Builds on

3 of 32 references.

References · 3

  1. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  2. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  3. 2024Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumorscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 5.

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  1. 2024A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyondcited by 16
  2. 2025The Missing Piece: A Case for Pre-Training in 3D Medical Object Detectioncited by 2
  3. 2025Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRIcited by 2
  4. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 2
  5. 2024Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascadecited by 1
  6. 2024A New Logic For Pediatric Brain Tumor Segmentationcited by 1
  7. 2025Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical datasetcited by 0
  8. 2025Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancementscited by 0
  9. 2026An Explainable AI-Driven Framework for Automated Brain Tumor Segmentation Using an Attention-Enhanced U-Netcited by 0
  10. 2026Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysiscited by 0
  11. 2026BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentationcited by 0
  12. 2026Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformationscited by 0
1 more
  1. 2026NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRIcited by 0