Reproducibility in Matrix and Tensor Decompositions: Focus on Model Match, Interpretability, and Uniqueness [0.03%]
矩阵和张量分解中的可重复性：关注模型匹配、解释性和唯一性
Tülay Adali,Furkan Kantar,M A B Siddique Akhonda et al.
Tülay Adali et al.
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement: An overview from a signal processing perspective [0.03%]
基于信号处理的生物医学图像重建与增强的无监督深度学习方法综述
Mehmet Akçakaya,Burhaneddin Yaman,Hyungjin Chung et al.
Mehmet Akçakaya et al.
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the diffic...
Light-Field Microscopy for Optical Imaging of Neuronal Activity: When Model-Based Methods Meet Data-Driven Approaches [0.03%]
基于模型方法与数据驱动策略在神经元活动光学成像中的结合——光场显微镜技术
Pingfan Song,Herman Verinaz Jadan,Carmel L Howe et al.
Pingfan Song et al.
Understanding how networks of neurons process information is one of the key challenges in modern neuroscience. A necessary step to achieve this goal is to be able to observe the dynamics of large populations of neurons over a large area of ...
Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning [0.03%]
结构化低秩算法：理论、磁共振应用及其与机器学习的联系
Mathews Jacob,Merry P Mani,Jong Chul Ye
Mathews Jacob
In this survey, we provide a detailed review of recent advances in the recovery of continuous domain multidimensional signals from their few non-uniform (multichannel) measurements using structured low-rank matrix completion formulation. Th...
Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery [0.03%]
用于磁共振成像的现成方法：使用去噪算法进行图像恢复
Rizwan Ahmad,Charles A Bouman,Gregery T Buzzard et al.
Rizwan Ahmad et al.
Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, the data...
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues [0.03%]
并行磁共振成像重建的深度学习方法：当前研究趋势及存在问题综述
Florian Knoll,Kerstin Hammernik,Chi Zhang et al.
Florian Knoll et al.
Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deep ...
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning [0.03%]
机器学习分布式随机优化中的渐近网络独立性
Shi Pu,Alex Olshevsky,Ioannis Ch Paschalidis
Shi Pu
We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning. Our focus is the so-called asymptotic network independence property, ...
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks [0.03%]
深度磁共振图像重建：逆问题遇到神经网络
Dong Liang,Jing Cheng,Ziwen Ke et al.
Dong Liang et al.
Image reconstruction from undersampled k-space data has been playing an important role in fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and has also shown potential in significantly accelerating MRI...
Computational MRI with Physics-based Constraints: Application to Multi-contrast and Quantitative Imaging [0.03%]
基于物理原理约束的计算磁共振成像及其在多对比度和定量成像中的应用研究
Jonathan I Tamir,Frank Ong,Suma Anand et al.
Jonathan I Tamir et al.
Compressed sensing takes advantage of low-dimensional signal structure to reduce sampling requirements far below the Nyquist rate. In magnetic resonance imaging (MRI), this often takes the form of sparsity through wavelet transform, finite ...
Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging [0.03%]
MRI重建中的线性可预测性：利用移不变傅里叶结构实现更快更好的成像
Justin P Haldar,Kawin Setsompop
Justin P Haldar