Axial super-resolution optical coherence tomography via complex-valued network

Abstract

Optical coherence tomography (OCT) is a fast and non-invasive optical interferometric imaging technique that can provide high-resolution cross-sectional images of biological tissues. OCT’s key strength is its depth resolving capability which remains invariant along the imaging depth and is determined by the axial resolution. The axial resolution is inversely proportional to the bandwidth of the OCT light source. Thus, the use of broadband light sources can effectively improve the axial resolution and however leads to an increased cost. In recent years, real-valued deep learning technique has been introduced to obtain super-resolution optical imaging. In this study, we proposed a complex-valued super-resolution network (CVSR-Net) to achieve an axial super-resolution for OCT by fully utilizing the amplitude and phase of OCT signal. The method was evaluated on three OCT datasets. The results show that the CVSR-Net outperforms its real-valued counterpart with a better depth resolving capability. Furthermore, comparisons were made between our network, six prevailing real-valued networks and their complex-valued counterparts. The results demonstrate that the complex-valued network exhibited a better super-resolution performance than its real-valued counterpart and our proposed CVSR-Net achieved the best performance. In addition, the CVSR-Net was tested on out-of-distribution domain datasets and its super-resolution performance was well maintained as compared to that on source domain datasets, indicating a good generalization capability.

Rumor Detection with A Novel Graph Neural Network Approach

“…Recent studies developed deep learning models, such as bidirectional transformers(BERT) [10](which are widely using in review tasks [13]), recurrent neural network(RNN), [27] recursive neural networks (RvNN) [39], Long-Short Term Memory (LSTM), generative adversarial network (GAN) [16,56], transformer [7,20] and Convolutional Neural Networks (CNN) [11,17], to learn sequential features from information propagation patterns over time. [1] These models also have widespread applications in other fields, such as data security [21,22], vision learning [18,52], material analysis [15,51] , compiling [33] and hardware designing [34], E-commerce [37], image segmentation [43], traffic controlling [38], communication [28,32] and Aerial Search [30] . These methods, however, only learn the correlations from local neighbors in the structure of information propagation while ignore the global structures of rumor dispersion.…”