mlsanityData to 7 Oct 2026

fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data

arXiv 2109.03812 ↗First posted 8 Sep 2021eess.IV cs.CV cs.LGcited by 29

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Abstract (arXiv)

Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medical imaging. The fastMRI dataset, unique in that it contains large volumes of raw MRI data, has enabled significant advances in accelerating MRI using deep learning-based reconstruction methods. While the impact of the fastMRI dataset on the field of medical imaging is unquestioned, the dataset currently lacks clinical expert pathology annotations, critical to addressing clinically relevant reconstruction frameworks and exploring important questions regarding rendering of specific pathology using such novel approaches. This work introduces fastMRI+, which consists of 16154 subspecialist expert bounding box annotations and 13 study-level labels for 22 different pathology categories on the fastMRI knee dataset, and 7570 subspecialist expert bounding box annotations and 643 study-level labels for 30 different pathology categories for the fastMRI brain dataset. The fastMRI+ dataset is open access and aims to support further research and advancement of medical imaging in MRI reconstruction and beyond.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 8 Sep 2021.

Builds on

3 of 14 references.

References · 3

  1. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  2. 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
  3. 2020Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challengecited by 59

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 7; 2024: 8; 2025: 2; 2026: 2.

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  1. 2021Score-based diffusion models for accelerated MRIcited by 308
  2. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  3. 2022MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusioncited by 66
  4. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Modelscited by 48
  5. 2022SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluationcited by 42
  6. 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstructioncited by 14
  7. 2023DMLR: Data-centric Machine Learning Research -- Past, Present and Futurecited by 11
  8. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  9. 2024Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learningcited by 8
  10. 2024Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Predictioncited by 7
  11. 2022Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRIcited by 6
  12. 2024Towards Universal Unsupervised Anomaly Detection in Medical Imagingcited by 6
17 more
  1. 2021End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstructioncited by 4
  2. 2024NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimationcited by 4
  3. 2021Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructioncited by 3
  4. 2023On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Diseasecited by 3
  5. 2024Denoising Diffusion Models for Anomaly Localization in Medical Imagescited by 3
  6. 2022Image Quality Assessment for Magnetic Resonance Imagingcited by 2
  7. 2022Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformerscited by 2
  8. 2022A Densely Interconnected Network for Deep Learning Accelerated MRIcited by 2
  9. 2023Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstructioncited by 2
  10. 2024The MRI Scanner as a Diagnostic: Image-less Active Samplingcited by 2
  11. 2024Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstructioncited by 2
  12. 2023On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosiscited by 1
  13. 2024Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representationscited by 1
  14. 2022NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Datacited by 0
  15. 2025Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstructioncited by 0
  16. 2026Vision-Language Model-Guided Deep Unrolling Enables Personalized, Fast MRIcited by 0
  17. 2026Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imagingcited by 0