arXiv subjects

Jiang Du

Publications and source records attributed to Jiang Du.

11 recordsLinked to original sources

arXiv2024

Consistent complete independence test in high dimensions based on Chatterjee correlation coefficient

In this article, we consider the complete independence test of high-dimensional data. Based on Chatterjee coefficient, we pioneer the development of quadratic test and extreme value test which possess good testing performance for oscillatory data, and establish the corresponding large sample properties under both null hypotheses and alternative hypotheses. In order to overcome the shortcomings of quadratic statistic and extreme value statistic, we propose a testing method termed as power enhancement test by adding a screening statistic to the quadratic statistic. The proposed method do not reduce the testing power under dense alternative hypotheses, but can enhance the power significantly under sparse alternative hypotheses. Three synthetic data examples and two real data examples are further used to illustrate the performance of our proposed methods.

math.ST↗

arXiv2021

Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation

Radiology report generation aims at generating descriptive text from radiology images automatically, which may present an opportunity to improve radiology reporting and interpretation. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss, which struggles to generate informative sentences for clinical diagnoses since normal findings dominate the datasets. To tackle this challenge and encourage more clinically-accurate text outputs, we propose a novel weakly supervised contrastive loss for medical report generation. Experimental results demonstrate that our method benefits from contrasting target reports with incorrect but semantically-close ones. It outperforms previous work on both clinical correctness and text generation metrics for two public benchmarks.

cs.CL↗

arXiv2021

Quantitative Parametric Mapping of Tissues Properties from Standard Magnetic Resonance Imaging Enabled by Deep Learning

Magnetic resonance imaging (MRI) offers superior soft tissue contrast and is widely used in biomedicine. However, conventional MRI is not quantitative, which presents a bottleneck in image analysis and digital healthcare. Typically, additional scans are required to disentangle the effect of multiple parameters of MR and extract quantitative tissue properties. Here we investigate a data-driven strategy Q^2 MRI (Qualitative and Quantitative MRI) to derive quantitative parametric maps from standard MR images without additional data acquisition. By taking advantage of the interdependency between various MRI parametric maps buried in training data, the proposed deep learning strategy enables accurate prediction of tissue relaxation properties as well as other biophysical and biochemical characteristics from a single or a few images with conventional T_1/T_2 weighting. Superior performance has been achieved in quantitative MR imaging of the knee and liver. Q^2 MRI promises to provide a powerful tool for a variety of biomedical applications and facilitate the next generation of digital medicine.

physics.med-ph↗

arXiv2019

Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning

The purpose of this work is to develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones magnetic resonance (MR) imaging, and to automatically determine MR relaxation times, namely the T1, T1$ρ$, and T2* parameters, which can be used to assess knee osteoarthritis (OA). Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by two experienced radiologists based on subtracted T1$ρ$-weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1$ρ$, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared.The models developed using ROIs provided by two radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1$ρ$, and T2* relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of two radiologists. The proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

eess.IV↗

arXiv2019

Quantitative Ultrasound and B-mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles

We investigate the usefulness of quantitative ultrasound (QUS) and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30 MHz probe. Next, the nerves were extracted to prepare histology sections. 85 fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin, and ultrasound data to calculate backscatter coefficient (-24.89 dB $\pm$ 8.31), attenuation coefficient (0.92 db/cm-MHz $\pm$ 0.04), Nakagami parameter (1.01 $\pm$ 0.18) and entropy (6.92 $\pm$ 0.83), as well as B-mode texture features obtained via the gray level co-occurrence matrix algorithm. Significant Spearman's rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R=-0.68), entropy (R=-0.51), and for several texture features. Our study demonstrates that QUS may potentially provide information on structural components of nerve fascicles.

physics.med-ph↗

arXiv2018

Finding a promising venture capital project with todim under probabilistic hesitant fuzzy circumstance

Considering the risk aversion for gains and the risk seeking for losses of venture capitalists, the TODIM has been chosen as the decision-making method. Moreover, group decision is an available way to avoid the limited ability and knowledge etc. of venture capitalists.Simultaneously, venture capitalists may be hesitant among several assessed values with different probabilities to express their real perceptionbecause of the uncertain decision-making environment. However, the probabilistic hesitant fuzzy information can solve such problems effectively. Therefore, the TODIM has been extended to probabilistic hesitant fuzzy circumstance for the sake of settling the decision-making problem of venture capitalists in this paper. Moreover, due to the uncertain investment environment, the criteria weights are considered as probabilistic hesitant fuzzyinformation as well. Then, a case study has been used to verify the feasibility and validity of the proposed TODIM.Also, the TODIM with hesitant fuzzy information has been carried out to analysis the same case.From the comparative analysis, the superiority of the proposed TODIM in this paper has already appeared.

econ.GN↗

arXiv2018

Perceptual Compressive Sensing

Compressive sensing (CS) works to acquire measurements at sub-Nyquist rate and recover the scene images. Existing CS methods always recover the scene images in pixel level. This causes the smoothness of recovered images and lack of structure information, especially at a low measurement rate. To overcome this drawback, in this paper, we propose perceptual CS to obtain high-level structured recovery. Our task no longer focuses on pixel level. Instead, we work to make a better visual effect. In detail, we employ perceptual loss, defined on feature level, to enhance the structure information of the recovered images. Experiments show that our method achieves better visual results with stronger structure information than existing CS methods at the same measurement rate.

cs.CV↗

arXiv2018

Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction

Recently, deep learning methods have made a significant improvement in compressive sensing image reconstruction task. In the existing methods, the scene is measured block by block due to the high computational complexity. This results in block-effect of the recovered images. In this paper, we propose a fully convolutional measurement network, where the scene is measured as a whole. The proposed method powerfully removes the block-effect since the structure information of scene images is preserved. To make the measure more flexible, the measurement and the recovery parts are jointly trained. From the experiments, it is shown that the results by the proposed method outperforms those by the existing methods in PSNR, SSIM, and visual effect.

cs.CV↗

arXiv2018

Full Image Recover for Block-Based Compressive Sensing

Recent years, compressive sensing (CS) has improved greatly for the application of deep learning technology. For convenience, the input image is usually measured and reconstructed block by block. This usually causes block effect in reconstructed images. In this paper, we present a novel CNN-based network to solve this problem. In measurement part, the input image is adaptively measured block by block to acquire a group of measurements. While in reconstruction part, all the measurements from one image are used to reconstruct the full image at the same time. Different from previous method recovering block by block, the structure information destroyed in measurement part is recovered in our framework. Block effect is removed accordingly. We train the proposed framework by mean square error (MSE) loss function. Experiments show that there is no block effect at all in the proposed method. And our results outperform 1.8 dB compared with existing methods.

cs.CV↗

arXiv2017

Adaptive Measurement Network for CS Image Reconstruction

Conventional compressive sensing (CS) reconstruction is very slow for its characteristic of solving an optimization problem. Convolu- tional neural network can realize fast processing while achieving compa- rable results. While CS image recovery with high quality not only de- pends on good reconstruction algorithms, but also good measurements. In this paper, we propose an adaptive measurement network in which measurement is obtained by learning. The new network consists of a fully-connected layer and ReconNet. The fully-connected layer which has low-dimension output acts as measurement. We train the fully-connected layer and ReconNet simultaneously and obtain adaptive measurement. Because the adaptive measurement fits dataset better, in contrast with random Gaussian measurement matrix, under the same measuremen- t rate, it can extract the information of scene more efficiently and get better reconstruction results. Experiments show that the new network outperforms the original one.

cs.CV↗

arXiv2015

S-Store: Streaming Meets Transaction Processing

Stream processing addresses the needs of real-time applications. Transaction processing addresses the coordination and safety of short atomic computations. Heretofore, these two modes of operation existed in separate, stove-piped systems. In this work, we attempt to fuse the two computational paradigms in a single system called S-Store. In this way, S-Store can simultaneously accommodate OLTP and streaming applications. We present a simple transaction model for streams that integrates seamlessly with a traditional OLTP system. We chose to build S-Store as an extension of H-Store, an open-source, in-memory, distributed OLTP database system. By implementing S-Store in this way, we can make use of the transaction processing facilities that H-Store already supports, and we can concentrate on the additional implementation features that are needed to support streaming. Similar implementations could be done using other main-memory OLTP platforms. We show that we can actually achieve higher throughput for streaming workloads in S-Store than an equivalent deployment in H-Store alone. We also show how this can be achieved within H-Store with the addition of a modest amount of new functionality. Furthermore, we compare S-Store to two state-of-the-art streaming systems, Spark Streaming and Storm, and show how S-Store matches and sometimes exceeds their performance while providing stronger transactional guarantees.

cs.DB↗