mlsanityData to 14 Oct 2026

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)

arXiv 2305.19369 ↗First posted 30 May 2023eess.IV cs.CV physics.med-phcited by 51

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Abstract (arXiv)

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). Builds on 5 of its 35 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

5 of 35 references.

References · 5

  1. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  3. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  4. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25
  5. 2019Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulatorcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 19; 2025: 18; 2026: 12.

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  1. 2024The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRIcited by 69
  2. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  3. 2024How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentationcited by 28
  4. 2024AutoRG-Brain: Grounded Report Generation for Brain MRIcited by 24
  5. 2024Multi-modal Vision Pre-training for Medical Image Analysiscited by 23
  6. 2025UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysiscited by 12
  7. 2025In the Picture: Medical Imaging Datasets, Artifacts, and their Living Reviewcited by 8
  8. 2024Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Datacited by 6
  9. 2024Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challengecited by 5
  10. 2024Federated brain tumor segmentation: an extensive benchmarkcited by 5
  11. 2025A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentationcited by 5
  12. 2024An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentationcited by 4
38 more
  1. 20243D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challengecited by 4
  2. 2024Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatricscited by 4
  3. 2024On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networkscited by 3
  4. 2024Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentationcited by 3
  5. 2025SAM-aware Test-time Adaptation for Universal Medical Image Segmentationcited by 3
  6. 2025The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learningcited by 3
  7. 2026MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosiscited by 3
  8. 2023Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)cited by 2
  9. 2024Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosiscited by 2
  10. 2025Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy studycited by 2
  11. 2024Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentationcited by 1
  12. 2024Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Datasetcited by 1
  13. 2025Multi-encoder nnU-Net outperforms transformer models with self-supervised pretrainingcited by 1
  14. 2025Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Modelscited by 1
  15. 2025Dual Path Learning -- learning from noise and context for medical image denoisingcited by 1
  16. 2025EMedNeXt: An Enhanced Brain Tumor Segmentation Framework for Sub-Saharan Africa using MedNeXt V2 with Deep Supervisioncited by 1
  17. 2025Resource-Efficient Glioma Segmentation on Sub-Saharan MRIcited by 1
  18. 2025A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Developmentcited by 1
  19. 2025Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniquescited by 1
  20. 2026SRUG: A Fusion-Driven Generator Network for Medical Image Translationcited by 1
  21. 2026BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Featurescited by 1
  22. 2023Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Datacited by 0
  23. 2024Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNetcited by 0
  24. 2024SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentationcited by 0
  25. 2024If you can describe it, they can see it: Cross-Modal Learning of Visual Concepts from Textual Descriptionscited by 0
  26. 2025How We Won BraTS-SSA 2025: Brain Tumor Segmentation in the Sub-Saharan African Population Using Segmentation-Aware Data Augmentation and Model Ensemblingcited by 0
  27. 2025Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRIcited by 0
  28. 2025Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentationcited by 0
  29. 2025Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performancecited by 0
  30. 2025Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learningcited by 0
  31. 2026Bridging the Applicator Gap with Data-Doping:Dual-Domain Learning for Precise Bladder Segmentation in CT-Guided Brachytherapycited by 0
  32. 2026Training Beyond Convergence: Grokking nnU-Net for Glioma Segmentation in Sub-Saharan MRIcited by 0
  33. 2026EviATTA: Evidential Active Test-Time Adaptation for Medical Segment Anything Modelscited by 0
  34. 2026Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Datasetcited by 0
  35. 2026Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalitiescited by 0
  36. 2026CoRE: Concept-Reasoning Expansion for Continual Brain Lesion Segmentationcited by 0
  37. 2026Knowledge Transfer Scaling Laws for 3D Medical Imagingcited by 0
  38. 2026Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imagingcited by 0

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