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The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) Dataset

arXiv 2109.00356 ↗First posted 30 Aug 2021cs.CV eess.IVcited by 42

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Abstract (arXiv)

Here we present the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 500 subjects with histopathologically-proven diffuse gliomas who were imaged with a standardized 3 Tesla preoperative brain tumor MRI protocol featuring predominantly 3D imaging, as well as advanced diffusion and perfusion imaging techniques. The dataset also includes isocitrate dehydrogenase (IDH) mutation status for all cases and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status for World Health Organization (WHO) grade III and IV gliomas. The UCSF-PDGM has been made publicly available in the hopes that researchers around the world will use these data to continue to push the boundaries of AI applications for diffuse gliomas.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Builds on 1 of its 15 references that are in the map. First posted on arXiv 30 Aug 2021.

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1 of 15 references.

References · 1

  1. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 6; 2025: 16; 2026: 14.

42 papers.

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  1. 2024Multi-modal Vision Pre-training for Medical Image Analysiscited by 22
  2. 2022Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Reportcited by 18
  3. 2023The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)cited by 11
  4. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  5. 2024Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastomacited by 9
  6. 2025Towards Generalisable Foundation Models for Brain MRIcited by 8
  7. 2025MM-DINOv2: Adapting Foundation Models for Multi-Modal Medical Image Analysiscited by 6
  8. 2025Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imagingcited by 5
  9. 2026MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflowscited by 5
  10. 2023A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaborationcited by 4
  11. 2025A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastomacited by 4
  12. 2026Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysiscited by 4
30 more
  1. 2023Provable Multi-instance Deep AUC Maximization with Stochastic Poolingcited by 3
  2. 2024Deep Learning Based Apparent Diffusion Coefficient Map Generation from Multi-parametric MR Images for Patients with Diffuse Gliomascited by 3
  3. 2024T1-contrast Enhanced MRI Generation from Multi-parametric MRI for Glioma Patients with Latent Tumor Conditioningcited by 3
  4. 2025Revisiting Medical Image Retrieval via Knowledge Consolidationcited by 3
  5. 2025Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy studycited by 2
  6. 2026TumorFlow: Physics-Guided Longitudinal MRI Synthesis of Glioblastoma Growthcited by 2
  7. 2024VASARI-auto: equitable, efficient, and economical featurisation of glioma MRIcited by 1
  8. 2024Reliable Multi-modal Medical Image-to-image Translation Independent of Pixel-wise Aligned Datacited by 1
  9. 2025Synergistic Vision-Language Reinforcement Enables Scalable On-Demand Analysis across Diverse Clinical Taskscited by 1
  10. 2025Hierarchical Brain Structure Modeling for Predicting Genotype of Gliomacited by 1
  11. 2025MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencodercited by 1
  12. 2025TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networkscited by 1
  13. 2026VariViT: A Vision Transformer for Variable Image Sizescited by 1
  14. 2026Value-Monotonicity Matters: A Concordance Loss for Deep Survival Predictioncited by 1
  15. 2023Interpretable 2D Vision Models for 3D Medical Imagescited by 0
  16. 2025Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Gliomacited by 0
  17. 2025Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imagingcited by 0
  18. 2025NeuroRAD-FM: A Foundation Model for Neuro-Oncology with Distributionally Robust Trainingcited by 0
  19. 2025Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?cited by 0
  20. 2025Computational Imaging Meets LLMs: Zero-Shot IDH Mutation Prediction in Brain Gliomascited by 0
  21. 2025Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learningcited by 0
  22. 2026Learning Glioblastoma Tumor Heterogeneity Using Brain Inspired Topological Neural Networkscited by 0
  23. 2026Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performancecited by 0
  24. 2026Replay-Based Continual Learning for Physics-Informed Neural Operatorscited by 0
  25. 2026UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretationcited by 0
  26. 2026Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imagingcited by 0
  27. 2026How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Modelscited by 0
  28. 2026Unified MRI Brain Image Translation via Hierarchical Tumor Structure Comparisoncited by 0
  29. 2026Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesiscited by 0
  30. 2026Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRIcited by 0