Hulin Kuang

Hulin Kuang
Central South University | CSU · School of Information Science and Engineering

Doctor of Philosophy

About

51
Publications
15,841
Reads
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634
Citations
Introduction
Hulin Kuang currently works at Department School of Information Science and Engineering,Central South University,China. Hulin does research in medical image processing,intelligent transportation systems,machine learning and deep learning, computer vision.
Additional affiliations
September 2013 - January 2017
City University of Hong Kong
Position
  • PhD
September 2007 - July 2013
Wuhan University
Position
  • Bachelor and Mater student

Publications

Publications (51)
Preprint
Full-text available
Cervical abnormal cell detection is a challenging task as the morphological differences between abnormal cells and normal cells are usually subtle. To determine whether a cervical cell is normal or abnormal, cytopathologists always take surrounding cells as references and make careful comparison to identify its abnormality. To mimic these clinical...
Article
In recent years, deep learning as a state-of-the-art machine learning technique has made great success in histopathological image classification. However, most of deep learning approaches rely heavily on the substantial task-specific annotations, which require experienced pathologists’ manual labelling. As a result, they are laborious and time-cons...
Preprint
Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two tasks are based on single-task learning and rarely consider the correlation between them. Since the MMSE score, which is an impor...
Article
Accurate prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) is essential for clinical precision treatment. However, the existing methods of predicting pCR in esophageal cancer are based on the single stage data, which limits the performance of these methods. Effective fusion of the longitudinal data has th...
Article
Model selection for deep learning algorithms is an extremely important step in the process of extracting knowledge from limited data, especially in biomedical data. The common approach is to adopt cross-validation techniques to randomly divide a small subset of the training set as the validation data for parameter tuning and model selection. Howeve...
Article
The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma segmentation are important tasks for computer-aided diagnosis using preoperative multimodal magnetic resonance imaging (MRI). The two tasks are ongoing challenges due to the significant inter-tumor and intra-tumor heterogeneity. The existing methods to address them are mo...
Article
Effective fusion of multimodal magnetic resonance imaging (MRI) is of great significance to boost the accuracy of glioma grading thanks to the complementary information provided by different imaging modalities. However, how to extract the common and distinctive information from MRI to achieve complementarity is still an open problem in information...
Article
Full-text available
Background and purpose: Multiphase computed tomographic angiography (mCTA) provides time variant images of pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict tissue perfusion and infarction from mCTA source images. Methods: 284 patients with AIS were included fro...
Article
Detecting early infarct (EI) plays an essential role in patient selection for reperfusion therapy in the management of acute ischemic stroke (AIS). EI volume at acute or hyperacute stage can be measured using advanced pre-treatment imaging, such as MRI and CT perfusion. In this study, a novel multi-task learning approach, EIS-Net, is proposed to se...
Article
Full-text available
Background and Purpose—Prediction of infarct extent among patients with acute ischemic stroke (AIS) using computed tomography perfusion (CTP) is defined by predefined discrete CTP thresholds. Our objective is to develop a threshold-free CTP based machine learning model to predict follow-up infarct in AIS patients. Methods—68 patients from the PRove...
Article
Full-text available
Due to the low luminance in nighttime traf c images, image features are not salient, making tasks in intelligent transportation systems such as nighttime vehicle detection challenging. Recently, convolutional neural network based methods have been developed for low-light image enhancement. Most of these methods are supervised and require high-light...
Article
Full-text available
Background and objective: Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to develop an automated segmentation method for accurately segmenting follow-up ischemic and hemorrhagic lesion from multislice non-contrast CT (NCCT) volumes of AIS patients. Meth...
Preprint
Full-text available
Background: Multiphase CT-Angiography (mCTA) provides time variant images of the pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict infarct, penumbra and tissue perfusion from mCTA source images. Methods: 284 patients with AIS were included from the PRoveIT study. A...
Article
Full-text available
Background: Identifying extent of infarcted brain tissue at baseline plays a crucial role in management of patients with acute ischemic stroke (AIS). Patients with extensive infarction are unlikely to benefit from thrombolysis or thrombectomy procedures. Purpose: Detect and quantitate infarction automatically on non-contrast-enhanced CT scans in p...
Article
Full-text available
Background: Manual segmentations of intracranial hemorrhage (ICrH) on non-contrast CT (NCCT) images are the gold-standard in measuring hematoma growth but are prone to rater variability. Aims: We demonstrate that a convex optimization-based interactive segmentation approach can accurately and reliably measure ICrH growth. Methods: Baseline and 1...
Article
Full-text available
Background: The Alberta Stroke Program Early CT Score (ASPECTS) is a systematic method of assessing extent of early ischemic change on Non-Contrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our objective was to validate an automated ASPECTS scoring method we recently developed on a large dataset. Materials and Methods: We retrospecti...
Chapter
Cerebral infarct volume measured in follow-up non-contrast CT (NCCT) scans is an important radiologic outcome measure evaluating the effectiveness of endovascular therapy of acute ischemic stroke (AIS) patients. In this paper, a dense Multi-Path Contextual Generative Adversarial Network (MPC-GAN) is proposed to automatically segment ischemic infarc...
Article
Full-text available
Background and Purpose— Computed tomographic perfusion (CTP) thresholds associated with follow-up brain infarction may differ by time from symptom onset to imaging and reperfusion. We confirm CTP thresholds over time to imaging and reperfusion in patients with acute ischemic stroke from the HERMES collaboration (Highly Effective Reperfusion Evaluat...
Article
Purpose: Cerebral infarct volume observed in follow-up noncontrast computed tomography (NCCT) scans of acute ischemic stroke (AIS) patients is as an important radiologic outcome measure of the effectiveness of endovascular therapy (EVT). In this paper, our aim is to propose a semiautomated segmentation approach that can accurately measure ischemic...
Article
Full-text available
Cerebral infarct volume (CIV) measured from follow-up non contrast CT (NCCT) scans of acute ischemic stroke (AIS) patients is an important radiologic outcome measure of the effectiveness of ischemic stroke treatment. Post-treatment CIV in NCCT of AIS patients typically includes ischemic infarct only. In around 10% of AIS patients, however, hemorrha...
Article
Background and Objective: Non-Contrast Computer tomography (NCCT) and CT angiography (CTA) are the most used and widely acceptable imaging modalities in clinical practice for the diagnosis and treatment of acute ischemic stroke (AIS) patients. Brain extraction of CT/CTA images plays an essential role in stroke imaging research. There is no robust a...
Article
Full-text available
Purpose – To determine the best radiomics based features of thrombus on non-contrast computed tomography (NCCT) and CT angiography (CTA) associated with recanalization with intravenous (IV) alteplase in patients with acute ischemic stroke (AIS) and proximal intracranial thrombi. Materials and Methods – Using a nested case control design, 67 patien...
Article
Full-text available
Background and Purpose: To develop an automated Alberta Stroke Program Early CT Score (ASPECTS) scoring method to objectively assess non-contrast CT (NCCT) scans of acute ischemic stroke (AIS) patients. Materials and Methods: We collected 257 AIS patient NCCT images with thickness of 5mm (<8 hours from onset to scans) followed by diffusion weighted...
Article
Night-time vehicle detection is essential in building intelligent transportation systems (ITS) for road safety. Most of current night-time vehicle detection approaches focus on one or two classes of vehicles. In this paper, we present a novel multiclass vehicle detection system based on tensor decomposition and object proposal. Commonly used featur...
Chapter
Full-text available
Cerebral infarct volume observed from follow-up non contrast CT (NCCT) scans is an important radiologic outcome measurement of the effectiveness of acute ischemic patient treatment. Post-treatment infarct typically includes ischemic infarct only. But in around 10% of ischemic stroke patients, intracerebral hemorrhage is present along with ischemic...
Article
Nighttime vehicle detection is part of an intelligent transportation system for road safety, driving navigation, and surveillance at night. However, previous nighttime vehicle detection methods only deal with a single class or two classes of vehicles. This paper presents an effective system based on cascade feature selection and a coarse-to-fine me...
Article
In this paper, we relate the operation of image dynamic range adjustment to the following two tasks: (1) for a high dynamic range (HDR) image, its dynamic range will be mapped to the available dynamic range of display devices, and (2) for a low dynamic range (LDR) image, its distribution of intensity will be extended to adequately utilize the full...
Article
Objective: The Alberta Stroke Program Early CT Score (ASPECTS) is widely used to assess and diagnose acute ischemic stroke (AIS) patients. However, reliability of ASPECTS scoring is poor among physicians with limited expertise. We hypothesize that reliability for ASPECTS scoring can be improved by using algorithm enhanced gray-white matter (AEGWM)...
Article
Objective: The Alberta Stroke Program Early CT Score (ASPECTS) method has been widely used to assess non-contrast CT scans from acute ischemic stroke (AIS) patients. Although the ASPECTS is a simple and systematic approach, ASPECTS scoring accuracy and reliability is still a challenge to clinicians, especially with limited experience. The objective...
Article
Introduction: As image interpretation is a visual perception task, it is affected by context and task structure. The non-contrast CT Alberta Stroke Program Early CT Score (ASPECTS) is considered an objective radiologic measure of the extent of ischemic change in cases of acute ischemic stroke. We hypothesize that variability in ASPECTS reading is b...
Poster
Objective: In acute ischemic stroke (AIS), thrombus lysis and early restoration of blood flow to ischemic brain is the goal of reperfusion therapy. We hypothesize that image texture analysis of thrombus using non-contrast CT (NCCT) and CTA can predict early recanalization with IV alteplase in AIS patients with ICA/M1 MCA thrombi. Methods: We used a...
Article
Object proposal is one of the most key pre-processing steps for nighttime vehicle detection systems in intelligent transportation systems. However, most current object proposal methods are developed on daytime data sets, and these methods demonstrate unsatisfactory results when they are used on nighttime images. Therefore, this paper presents a nov...
Article
Detecting vehicle turn signals at night is critical for both assistant driving systems and autonomous driving systems. In this paper, we propose a novel method that consists of detection and tracking modules to achieve a high level of robustness. For nighttime vehicle detection, a Nakagami-image-based method is used to locate the regions containing...
Data
Hong Kong multi-class Night-time vehicle Dataset: training samples of car, taxi, bus and minibus, detection images and groundtruths, and the images for object proposals
Article
In nighttime images, vehicle detection is a challenging task because of low contrast and luminosity. In this article, the authors combine a novel region-of-interest (ROI) extraction approach that fuses vehicle light detection and object proposals together with a nighttime image enhancement approach based on improved multiscale retinex to extract ac...
Article
We describe an object classification method based on weighted score-level feature fusion using learned weights. Our method is able to recognize 20 object classes in a customized fruit dataset. Although the fusion of multiple features is commonly used to distinguish variable object classes, the optimal combination of features is not well defined. Mo...
Article
This paper presents an effective nighttime vehicle detection system that combines a novel bioinspired image enhancement approach with a weighted feature fusion technique. Inspired by the retinal mechanism in natural visual processing, we develop a nighttime image enhancement method by modeling the adaptive feedback from horizontal cells and the cen...
Data
training images, testing images and groundtruths of testing images of the multi-class fruit dataset for fruit detection
Data
part of the testing images in the multi-class fruit dataset for detection
Data
the second part of the multi-class fruit dataset for detection
Data
This fruit dataset contains 20 classes of fruit. there are training and testing samples that are images containing single fruit. This fruit dataset supports the manuscript "fruit classification based on weight score level feature fusion" submitted to Journal of Electronic Imaging
Conference Paper
In this paper a novel approach based on multiple color channels is proposed for multi-class fruit detection. Multi-feature fusion of global color histogram, Local Binary Pattern (LBP), Histogram of Oriented Gradient (HOG), and LBP based on Gabor wavelets (GaborLBP) is utilized to improve fruit recognition. These four features are extracted on multi...
Article
A two-stage detection method based on Adaboost and support vector machine (SVM) is proposed for the pedestrian detection problem in a single image, which uses the combination of coarse level and fine level detection to improve the accuracy of the detector. The coarse level pedestrian detector makes use of the four direction features (FDF) and the g...

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Projects

Projects (3)
Project
Automatically segment ischemic lesion or hemorrhage from NCCT images
Project
Automatically assign ASPECTS scores on NCCT images of acute ischemic patients using machine learning