mlsanityData to 14 Oct 2026

An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset

arXiv 2110.00715 ↗First posted 2 Oct 2021cs.CV math.OCcited by 18

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Abstract (arXiv)

Purpose: This work aims at developing a generalizable MRI reconstruction model in the meta-learning framework. The standard benchmarks in meta-learning are challenged by learning on diverse task distributions. The proposed network learns the regularization function in a variational model and reconstructs MR images with various under-sampling ratios or patterns that may or may not be seen in the training data by leveraging a heterogeneous dataset. Methods: We propose an unrolling network induced by learnable optimization algorithms (LOA) for solving our nonconvex nonsmooth variational model for MRI reconstruction. In this model, the learnable regularization function contains a task-invariant common feature encoder and task-specific learner represented by a shallow network. To train the network we split the training data into two parts: training and validation, and introduce a bilevel optimization algorithm. The lower-level optimization trains task-invariant parameters for the feature encoder with fixed parameters of the task-specific learner on the training dataset, and the upper-level optimizes the parameters of the task-specific learner on the validation dataset. Results: The average PSNR increases significantly compared to the network trained through conventional supervised learning on the seen CS ratios. We test the result of quick adaption on the unseen tasks after meta-training and in the meanwhile saving half of the training time; Conclusion: We proposed a meta-learning framework consisting of the base network architecture, design of regularization, and bi-level optimization-based training. The network inherits the convergence property of the LOA and interpretation of the variational model. The generalization ability is improved by the designated regularization and bilevel optimization-based training algorithm.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 42 of its 74 references that are in the map. First posted on arXiv 2 Oct 2021.

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42 of 74 references.

References · 42

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 1; 2024: 10; 2025: 2; 2026: 1.

18 papers.

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  1. 2024Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approachcited by 24
  2. 2022A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesiscited by 16
  3. 2022An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identificationcited by 14
  4. 2024Application of Multimodal Fusion Deep Learning Model in Disease Recognitioncited by 12
  5. 2024Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Datacited by 11
  6. 2024Enhancing Medical Imaging with GANs Synthesizing Realistic Images from Limited Datacited by 11
  7. 2024Style Transfer: From Stitching to Neural Networkscited by 8
  8. 2024Multi-task Magnetic Resonance Imaging Reconstruction using Meta-learningcited by 7
  9. 2022CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoisercited by 6
  10. 2024Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networkscited by 5
  11. 2024Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanismscited by 4
  12. 2023Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentationcited by 3
6 more
  1. 2024A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learningcited by 3
  2. 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Modelscited by 2
  3. 2022Distributed Dynamic Safe Screening Algorithms for Sparse Regularizationcited by 1
  4. 2025Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectorscited by 0
  5. 2025Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimizationcited by 0
  6. 2026Transferable Optimization Network for Cross-Domain Image Reconstructioncited by 0