A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Bian, Wanyu; Zhang, Qingchao; Ye, Xiaojing; Chen, Yunmei

doi:10.1007/978-3-031-16446-0_34

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…To make the distribution more like one-hot we used Gumble Softmax to generate the distribution. Reverse engineering was ran for each sentiment class [0,1] x trigger length [1,3,8] x 3 repeats for diverse triggers.…”

Section: Enumerate Trigger Detectormentioning

confidence: 99%

Analyzing Multi-Head Attention on Broken BERT Models

Wang

2024

Preprint

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This project investigates the behavior of multi-head attention in Transformer models, specifically focusing on the differences between benign and trojan models in the context of sentiment analysis. Trojan attacks cause models to perform normally on clean inputs but exhibit misclassifications when presented with inputs containing predefined triggers. We characterize attention head functions in trojan and benign models, identifying specific 'trojan' heads and analyzing their behavior.

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Section: Enumerate Trigger Detectormentioning

confidence: 99%

Analyzing Multi-Head Attention on Broken BERT Models

Wang

2024

Preprint

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“…[30][31][32][33][34] To improve the interpretability of the relation between the topology of the deep model and reconstruction results, a new emerging class of deep learning-based methods known as learnable optimization algorithms (LOA) have attracted much attention e.g. [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] LOA was proposed to map existing optimization algorithms to structured networks where each phase of the networks correspond to one iteration of an optimization algorithm.…”

Section: Optimization-based Network Unrolling Algorithms For Mri Reco...mentioning

confidence: 99%

“…This subsection introduces a provable learnable optimization algorithm 48 for joint MRI reconstruction and synthesis. Given the partial k-space data {f 1 , f 2 } of the source modalities (e.g.…”

Section: Variational Model For Joint Reconstruction and Synthesismentioning

confidence: 99%

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

Bian¹,

Chen²,

Ye³

et al. 2021

Preprint

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(1) 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. (2) 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. (3) Results: The PSNR increases 1.5 dB on average 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, the average PSNR arises 1.22 dB compared to the conventional learning procedure that is directly trained on the unseen CS ratios in the meanwhile saving half of the training time. The average PSNR arises 1.87 dB for unseen sampling patterns comparing to conventional learning; (4) 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.

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Learned Alternating Minimization Algorithm for Dual-Domain Sparse-View CT Reconstruction

Ding,

Zhang,

Wang

et al. 2023

Lecture Notes in Computer Science

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Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block optimization by synergizing data-driven and classical techniques with proven convergence. LAMA is naturally induced by a variational model with learnable regularizers in both data and image domains, parameterized as composite functions of neural networks trained with domain-specific data. We allow these regularizers to be nonconvex and nonsmooth to extract features from data effectively. We minimize the overall objective function using Nesterov's smoothing technique and residual learning architecture. It is demonstrated that LAMA reduces network complexity, improves memory efficiency, and enhances reconstruction accuracy, stability, and interpretability. Extensive experiments show that LAMA significantly outperforms state-ofthe-art methods on popular benchmark datasets for Computed Tomography.

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A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Cited by 10 publications

References 23 publications

Analyzing Multi-Head Attention on Broken BERT Models

Analyzing Multi-Head Attention on Broken BERT Models

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

Learned Alternating Minimization Algorithm for Dual-Domain Sparse-View CT Reconstruction

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