Search Results for author: Hua Li

Found 47 papers, 2 papers with code

Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification

no code implementations ECNLP (ACL) 2022 Yang Liu, Varnith Chordia, Hua Li, Siavash Fazeli Dehkordy, Yifei Sun, Vincent Gao, Na Zhang

To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text.

Multi-Label Classification Text Generation +1

On the impact of incorporating task-information in learning-based image denoising

no code implementations23 Nov 2022 Kaiyan Li, Hua Li, Mark A. Anastasio

The task-component was designed to measure the performance of a numerical observer (NO) on a signal detection task.

Computed Tomography (CT) Image Denoising +2

Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework

no code implementations11 Oct 2022 Zong Fan, Ping Gong, Shanshan Tang, Christine U. Lee, Xiaohui Zhang, Pengfei Song, Shigao Chen, Hua Li

By use of the attention mechanism, the auxiliary lesion-aware network can optimize multi-scale intermediate feature maps and extract rich semantic information to improve classification and localization performance.

Classification Lesion Detection

Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model

no code implementations8 Oct 2022 Hua Li, Wenya Luo, Zhidong Bai, Huanchao Zhou, Zhangni Pu

In this paper, we propose an improved linear discriminant analysis, called spectrally-corrected and regularized linear discriminant analysis (SCRLDA).

Stereo Superpixel Segmentation Via Decoupled Dynamic Spatial-Embedding Fusion Network

no code implementations17 Aug 2022 Hua Li, Junyan Liang, Ruiqi Wu, Runmin Cong, Junhui Wu, Sam Tak Wu Kwong

To decouple stereo disparity information and spatial information, the spatial information is temporarily removed before fusing the features of stereo image pairs, and a decoupled stereo fusion module (DSFM) is proposed to handle the stereo features alignment as well as occlusion problems.

object-detection Object Detection +1

The Counterfactual-Shapley Value: Attributing Change in System Metrics

no code implementations17 Aug 2022 Amit Sharma, Hua Li, Jian Jiao

Specifically, we propose a method to estimate counterfactuals using time-series predictive models and construct an attribution score, CF-Shapley, that is consistent with desirable axioms for attributing an observed change in the output metric.

Time Series Analysis

Application of DatasetGAN in medical imaging: preliminary studies

no code implementations27 Feb 2022 Zong Fan, Varun Kelkar, Mark A. Anastasio, Hua Li

Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging.

Image Segmentation Semantic Segmentation

STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems

no code implementations10 Feb 2022 Jinlei Zhang, Hua Li, Lixing Yang, Guangyin Jin, Jianguo Qi, Ziyou Gao

To overcome these limitations, we propose a novel deep learning-based spatiotemporal graph generative adversarial network (STG-GAN) model with higher prediction accuracy, higher efficiency, and lower memory occupancy to predict short-term passenger flows of the URT network.

Gaussian-Hermite Moment Invariants of General Multi-Channel Functions

no code implementations3 Jan 2022 Hanlin Mo, Hua Li, Guoying Zhao

Then, we design a structural framework to generate Gaussian-Hermite moment invariants for these two transform models systematically.

Image Classification Template Matching

A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods

no code implementations22 Oct 2021 Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio

Specifically, a hybrid approach is developed that combines a multi-task convolutional neural network and a Markov-Chain Monte Carlo (MCMC) method in order to approximate the IO for detection-estimation tasks.

Impact of deep learning-based image super-resolution on binary signal detection

no code implementations6 Jul 2021 Xiaohui Zhang, Varun A. Kelkar, Jason Granstedt, Hua Li, Mark A. Anastasio

The presented study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.

Image Super-Resolution

Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks

no code implementations27 Jun 2021 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

AmbientGANs established using the proposed training procedure are systematically validated in a controlled way using computer-simulated magnetic resonance imaging (MRI) data corresponding to a stylized imaging system.

Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks

no code implementations28 Apr 2021 Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio

The performance of the ideal observer (IO) and common linear numerical observers are quantified and detection efficiencies are computed to assess the impact of the denoising operation on task performance.

Image Denoising

Advancing the AmbientGAN for learning stochastic object models

no code implementations30 Jan 2021 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Jason L. Granstedt, Hua Li, Mark A. Anastasio

Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks.

Geometric Moment Invariants to Motion Blur

no code implementations21 Jan 2021 Hongxiang Hao., Hanlin Mo., Hua Li

In this paper, we focus on removing interference of motion blur by the derivation of motion blur invariants. Unlike earlier work, we don't restore any blurred image.

Image Retrieval Retrieval +1

Superpixel Segmentation Based on Spatially Constrained Subspace Clustering

no code implementations11 Dec 2020 Hua Li, Yuheng Jia, Runmin Cong, Wenhui Wu, Sam Kwong, Chuanbo Chen

Consequently, we devise a spatial regularization and propose a novel convex locality-constrained subspace clustering model that is able to constrain the spatial adjacent pixels with similar attributes to be clustered into a superpixel and generate the content-aware superpixels with more detailed boundaries.


A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images

no code implementations2 Oct 2020 Chongyi Li, Runmin Cong, Chunle Guo, Hua Li, Chunjie Zhang, Feng Zheng, Yao Zhao

In this paper, we propose a novel Parallel Down-up Fusion network (PDF-Net) for SOD in optical RSIs, which takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds.

object-detection Object Detection +1

A multilayer interstitial fluid flow along vascular adventitia

no code implementations23 Sep 2020 Hongyi Li, You Lv, Xiaoliang Chen, Bei Li, Qi Hua, Fusui Ji, Yajun Yin, Hua Li

In real-time observations, the calculated velocity of a continuous ISF flow along fibers of a PACT pathway was 3. 6-15. 6 mm/sec.

Two-stage short-term wind power forecasting algorithm using different feature-learning models

no code implementations31 May 2020 Jiancheng Qin, Jin Yang, Ying Chen, Qiang Ye, Hua Li

Considering the overfitting issue, we propose a new moving window-based algorithm using a validation set in the first stage to update the training data in both stages with two different moving window processes. Experiments were conducted at three wind farms, and the results demonstrate that the model with single input multiple output structure obtains better forecasting accuracy compared to existing models.

Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs

no code implementations29 May 2020 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements.

Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods

no code implementations29 May 2020 Weimin Zhou, Hua Li, Mark A. Anastasio

When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve.

Learning Numerical Observers using Unsupervised Domain Adaptation

no code implementations3 Feb 2020 Shenghua He, Weimin Zhou, Hua Li, Mark A. Anastasio

In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones.

Image Quality Assessment Unsupervised Domain Adaptation

Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements

no code implementations26 Jan 2020 Weimin Zhou, Sayantan Bhadra, Frank J. Brooks, Hua Li, Mark A. Anastasio

However, because medical imaging systems record imaging measurements that are noisy and indirect representations of object properties, GANs cannot be directly applied to establish stochastic models of objects to-be-imaged.

Dual affine moment invariants

no code implementations19 Nov 2019 You Hao, Hanlin Mo, Qi Li, He Zhang, Hua Li

In this paper, we propose a general framework to derive moment invariants under DAT for objects in M-dimensional space with N channels, which can be called dual-affine moment invariants (DAMI).

Rotation Differential Invariants of Images Generated by Two Fundamental Differential Operators

no code implementations13 Nov 2019 Hanlin Mo, Hua Li

As far as we know, no previous papers have published so many explicit forms of high-order rotation differential invariants of images.

Texture Classification Vocal Bursts Valence Prediction

Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods

no code implementations15 May 2019 Weimin Zhou, Hua Li, Mark A. Anastasio

For binary signal detection tasks, the Bayesian Ideal Observer (IO) sets an upper limit of observer performance and has been advocated for use in optimizing medical imaging systems and data-acquisition designs.

Automatic microscopic cell counting by use of deeply-supervised density regression model

no code implementations4 Mar 2019 Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark Anastasio, Hua Li

Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to subjective errors, and automatic counting can be less accurate than desired.

Automatic Cell Counting regression

Differential and integral invariants under Mobius transformation

no code implementations30 Aug 2018 He Zhang, Hanlin Mo, You Hao, Qi Li, Hua Li

According to the Liouville Theorem, an important part of the conformal transformation is the Mobius transformation, so we focus on Mobius transformation and propose two differential expressions that are invariable under 2-D and 3-D Mobius transformation respectively.

Convolutional neural network based automatic plaque characterization from intracoronary optical coherence tomography images

no code implementations10 Jul 2018 Shenghua He, Jie Zheng, Akiko Maehara, Gary Mintz, Dalin Tang, Mark Anastasio, Hua Li

Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images.

Classification feature selection +2

Fast and Efficient Calculations of Structural Invariants of Chirality

no code implementations20 Oct 2017 He Zhang, Hanlin Mo, You Hao, Shirui Li, Hua Li

And the five chiral invariants have four characteristics:(1) They play an important role in the detection of symmetry, especially in the treatment of 'false zero' problem.

Symmetry Detection

Image Projective Invariants

no code implementations19 Jul 2017 Erbo Li, Hanlin Mo, Dong Xu, Hua Li

In this paper, we propose relative projective differential invariants (RPDIs) which are invariant to general projective transformations.

Image Retrieval Retrieval

Shape-Color Differential Moment Invariants under Affine Transformations

no code implementations14 Jun 2017 Hanlin Mo, Shirui Li, You Hao, Hua Li

We propose the general construction formula of shape-color primitives by using partial differentials of each color channel in this paper.

General Classification Image Classification +1

A Kind of Affine Weighted Moment Invariants

no code implementations5 Jun 2017 Hanlin Mo, You Hao, Shirui Li, Hua Li

A new kind of geometric invariants is proposed in this paper, which is called affine weighted moment invariant (AWMI).

Image Retrieval Retrieval

Naturally Combined Shape-Color Moment Invariants under Affine Transformations

no code implementations31 May 2017 Ming Gong, You Hao, Hanlin Mo, Hua Li

We proposed a kind of naturally combined shape-color affine moment invariants (SCAMI), which consider both shape and color affine transformations simultaneously in one single system.

Reflection Invariant and Symmetry Detection

no code implementations30 May 2017 Erbo Li, Hua Li

Symmetry detection and discrimination are of fundamental meaning in science, technology, and engineering.

Object Recognition Retrieval +1

Isomorphism between Differential and Moment Invariants under Affine Transform

no code implementations20 May 2017 Erbo Li, Hua Li

The differential invariant is essential in understanding or describing some important phenomena or procedures in mathematics, physics, chemistry, biology or computer science etc.

Affine-Gradient Based Local Binary Pattern Descriptor for Texture Classiffication

no code implementations19 May 2017 You Hao, Shirui Li, Hanlin Mo, Hua Li

We present a novel Affine-Gradient based Local Binary Pattern (AGLBP) descriptor for texture classification.

feature selection General Classification +1

4d isip: 4d implicit surface interest point detection

no code implementations10 May 2017 Shirui Li, Alper Yilmaz, Changlin Xiao, Hua Li

The TSDF represents the distance between the spatial points and object surface points which is an implicit surface representation.

Interest Point Detection

Shape DNA: Basic Generating Functions for Geometric Moment Invariants

no code implementations7 Mar 2017 Erbo Li, Yazhou Huang, Dong Xu, Hua Li

Two fundamental building blocks or generating functions (GFs) for invariants are discovered, which are dot product and vector product of point vectors in Euclidean space.

Information Retrieval Retrieval

Indoor occupancy estimation from carbon dioxide concentration

1 code implementation20 Jul 2016 Chaoyang Jiang, Mustafa K. Masood, Yeng Chai Soh, Hua Li

The accuracy is up to 94 percent with a tolerance of four occupants.

Cannot find the paper you are looking for? You can Submit a new open access paper.