Search Results for author:
Found 52 papers, 3 papers with code
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification
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.
One Signal-Noise Separation based Wiener Filter for Magnetogastrogram
Considering the solutions to improve the signal-to-noise ratio (SNR) of Wiener filter output, there are few methods to separate the signals from the noises of the reference signal at the filter input.
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work.
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning
A series of out-of-distribution tests further confirmed the generality of our framework.
Compact Twice Fusion Network for Edge Detection
In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the compactness of the model.
A quality assurance framework for real-time monitoring of deep learning segmentation models in radiotherapy
An image domain shift detector was developed by utilizing a trained Denoising autoencoder (DAE) and two hand-engineered features.
WaterMask: Instance Segmentation for Underwater Imagery
Underwater image instance segmentation is a fundamental and critical step in underwater image analysis and understanding.
Ranked #1 on
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On the impact of incorporating task-information in learning-based image denoising
The task-component was designed to measure the performance of a numerical observer (NO) on a signal detection task.
Joint localization and classification of breast tumors on ultrasound images using a novel auxiliary attention-based framework
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.
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model
This paper proposes an improved linear discriminant analysis called spectrally-corrected and regularized LDA (SRLDA).
Stereo Superpixel Segmentation Via Decoupled Dynamic Spatial-Embedding Fusion Network
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.
The Counterfactual-Shapley Value: Attributing Change in System Metrics
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.
A novel adversarial learning strategy for medical image classification
Deep learning (DL) techniques have been extensively utilized for medical image classification.
Application of DatasetGAN in medical imaging: preliminary studies
Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging.
STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems
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
Then, we design a structural framework to generate Gaussian-Hermite moment invariants for these two transform models systematically.
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
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
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.
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
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
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.
Advancing the AmbientGAN for learning stochastic object models
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
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.
Superpixel Segmentation Based on Spatially Constrained Subspace Clustering
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.
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Adapting semantic segmentation models to new domains is an important but challenging problem.
Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images
In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images.
A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images
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.
A multilayer interstitial fluid flow along vascular adventitia
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
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
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
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.
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition
The dynamics of human skeletons have significant information for the task of action recognition.
Learning Numerical Observers using Unsupervised Domain Adaptation
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.
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
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
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
As far as we know, no previous papers have published so many explicit forms of high-order rotation differential invariants of images.
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods
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
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 microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression
A domain adaptation model (DAM) is built to map experimental images (the target domain) to the feature space of the source domain.
Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra
Machine learning has been used to develop the model.
Differential and integral invariants under Mobius transformation
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
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.
Fast and Efficient Calculations of Structural Invariants of Chirality
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.
Image Projective Invariants
In this paper, we propose relative projective differential invariants (RPDIs) which are invariant to general projective transformations.
Shape-Color Differential Moment Invariants under Affine Transformations
We propose the general construction formula of shape-color primitives by using partial differentials of each color channel in this paper.
A Kind of Affine Weighted Moment Invariants
A new kind of geometric invariants is proposed in this paper, which is called affine weighted moment invariant (AWMI).
Naturally Combined Shape-Color Moment Invariants under Affine Transformations
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
Symmetry detection and discrimination are of fundamental meaning in science, technology, and engineering.
Isomorphism between Differential and Moment Invariants under Affine Transform
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
We present a novel Affine-Gradient based Local Binary Pattern (AGLBP) descriptor for texture classification.
4d isip: 4d implicit surface interest point detection
The TSDF represents the distance between the spatial points and object surface points which is an implicit surface representation.
Shape DNA: Basic Generating Functions for Geometric Moment Invariants
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.
Indoor occupancy estimation from carbon dioxide concentration
The accuracy is up to 94 percent with a tolerance of four occupants.
