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Author

Qi Zhang

Bio: Qi Zhang is an academic researcher from Fudan University. The author has contributed to research in topics: Medicine & Ultrasound. The author has an hindex of 20, co-authored 100 publications receiving 1563 citations. Previous affiliations of Qi Zhang include Minjiang University & Duke University.


Papers
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Book ChapterDOI
Jinjie Wu1, Jun Shi1, Shihui Ying1, Qi Zhang1, Yan Li2 
17 Oct 2016
TL;DR: The experimental results on two histopathological image datasets show that GANet outperforms PCANet, while QGANet achieves the best performance for the classification of color Histopathological images.
Abstract: Feature representation is a key step for the classification of histopathological images. The principal component analysis network (PCANet) offers a new unsupervised feature learning algorithm for images via a simple deep network architecture. However, PCA is sensitive to noise and outliers, which may depress the representation learning of PCANet. Grassmann averages (GA) is a newly proposed dimensionality reduction algorithm, which is more robust and effective than PCA. Therefore, in this paper, we propose a GA network (GANet) algorithm to improve the robustness of learned features from images. Moreover, since quaternion algebra provides a mathematically elegant tool to well handle color images, a quaternion representation based GANet (QGANet) is developed to fuse color information and learn a superior representation for color histopathological images. The experimental results on two histopathological image datasets show that GANet outperforms PCANet, while QGANet achieves the best performance for the classification of color histopathological images.

4 citations

Proceedings ArticleDOI
01 Oct 2016
TL;DR: It can be concluded that the histological grade and subtype information could be estimated from the MRI image analysis.
Abstract: Glioma is one of the most common brain tumors with high mortality and its histological grading and typing is important both in therapeutic decision and prognosis evaluation. This paper aims at using the high-throughput image feature analysis method to estimate the histological grade and type of a patient by using Magnetic Resonance Imaging (MRI) instead of histological examination. The proposed method consists of the initial label definition, the region-of-interest delineation, the self-adaptive feature extraction, the feature subset selection, and the multi-class voting classification. Hereinto, a novel feature extraction strategy is designed, which could avoid the MRI scan diversity so as to get the robust feature extraction result and make the proposed framework more stable and effective. This method was validated on a database of 124 patients with the grade II to IV of 78, 25, and 21, and with astrocytoma, oligodendroglioma, oligoastrocytoma of 86, 16, and 22, respectively. We show that by using the leave-one-out cross-validation, the multi-class classification accuracy and macro average could reach 88.71%, 0.8362 respectively for the grade classification, and 70.97%, 0.5692 respectively for the type classification. It can be concluded that the histological grade and subtype information could be estimated from the MRI image analysis.

4 citations

Journal ArticleDOI
TL;DR: As a non-invasive imaging modality, the RTE could be potentially used in routine clinical practice for the detection of high-risk PCA to decrease unnecessary biopsies and reduce overtreatment.
Abstract: To examine the role of quantitative real-time elastography (RTE) features on differentiation between high-risk prostate cancer (PCA) and non-high-risk prostatic diseases in the initial transperineal biopsy setting. We retrospectively included 103 patients with suspicious PCA who underwent both RTE and initial transperineal prostate biopsy. Patients were grouped into high-risk and non-high-risk categories according to the D’Amico’s risk stratification. With computer assistance based on MATLAB programming, three features were extracted from RTE, i.e., the median hardness within peripheral gland (PG) (H med), the ratio of the median hardness within PG to that outside PG (H ratio), and the ratio of the hard area within PG to the total PG area (H ar). A multiple regression model incorporating an RTE feature, age, transrectal ultrasound finding, and prostate volume was used to identify markers for high-risk PCA. Forty-seven patients (45.6%) were diagnosed with PCA and 34 (33.0%) were diagnosed with high-risk PCA. Three RTE features were all statistically higher in high-risk PCA than in non-high-risk diseases (p < 0.001), indicating that the PGs in high-risk PCA patients were harder than those in non-high-risk patients. A high H ratio, high age, and low prostate volume were found to be independent markers for PCAs (p < 0.05), among which the high H ratio was the only independent marker for high-risk PCAs (p = 0.012). When predicting high-risk PCAs, the multiple regression achieved an area under receiver operating characteristic curve of 0.755, sensitivity of 73.5%, and specificity of 71.0%. The elevated hardness of PG identified high-risk PCA and served as an independent marker of high-risk PCA. As a non-invasive imaging modality, the RTE could be potentially used in routine clinical practice for the detection of high-risk PCA to decrease unnecessary biopsies and reduce overtreatment.

4 citations

Journal ArticleDOI
Zeju Li1, Yuanyuan Wang1, Jinhua Yu1, Yi Guo1, Qi Zhang2 
15 Sep 2017
TL;DR: It is indicated that glioblastoma in different age groups should have different pathologic, protein, or genic origins, which indicates that glooblastomas in differentAge groups present different radiomics-feature patterns with statistical significance.
Abstract: Glioblastoma is the most aggressive malignant brain tumor with poor prognosis. Radiomics is a newly emerging and promising technique to reveal the complex relationships between high-throughput medical image features and deep information of disease including pathology, biomarkers and genomics. An approach was developed to investigate the internal relationship between magnetic resonance imaging (MRI) features and the age-related origins of glioblastomas based on a quantitative radiomics method. A fully automatic image segmentation method was applied to segment the tumor regions from three dimensional MRI images. 555 features were then extracted from the image data. By analyzing large numbers of quantitative image features, some predictive and prognostic information could be obtained by the radiomics approach. 96 patients diagnosed with glioblastoma pathologically have been divided into two age groups (<45 and ≥45 years old). As expected, there are 101 features showing the consistency with the age gr...

4 citations

Journal ArticleDOI
Qi Zhang1, Yifang Lin1, Xinhua Liu1, Li Zhang1, Yan Zhang, Dong Zhao, Qi Lu, Jie Jia1 
TL;DR: In this paper, the authors compared elderly diabetic patients with DPN and without DPN, and found that the elderly DPN group showed worse thumb-middle fingertip pinch strength and thumb-little fingertip pressure in the dominant hand compared with the non-DPN group.
Abstract: Objective Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications of diabetes, leading to disability and decreased quality of life. In past research and clinical studies, the lower limb function of DPN patients was often the principal subject of research, with little attention given to the upper limb and hand. Our goal was to assess and compare hand function between elderly diabetic patients with DPN and without DPN. Methods A total of 52 diabetic patients were registered and underwent hand function assessments and electrodiagnostic tests. Dynamometer, pinch meter, Semmes Weinstein monofilaments, and the Purdue Pegboard Test (PPT) were used to assess the patients' grip strength, pinch strength, tactile sensory threshold, and hand dexterity. Results Compared with the non-DPN group, the elderly DPN group showed worse thumb-middle fingertip pinch strength and thumb-little fingertip pinch strength in the dominant hand (3.50 (2.50, 4.25) vs. 4.50 (3.00, 5.00), p = 0.019; 1.50 (1.00, 2.00) vs. 2.50 (2.00, 3.00), p < 0.001); the elderly DPN group displayed worse thumb-middle fingertip pinch strength, thumb-ring fingertip pinch strength, and thumb-little fingertip pinch strength in the nondominant hand (3.50 (2.00, 4.50) vs. 4.00 (3.00, 5.00), p = 0.013; 2.50 (1.25, 3.00) vs. 3.00 (2.50, 3.50), p = 0.033; 1.00 (0.75, 2.25) vs. 2.50 (2.00, 2.50), p < 0.001). The elderly DPN group scored lower than the non-DPN group on the PPT test of assembly (13.96 ± 5.18 vs. 16.96 ± 4.61, t = 2.212, p = 0.032). Conclusion Motor function limitation is the principal hand dysfunction in elderly patients with DPN, which is mainly manifested as a decline in fingertip pinch strength and a decrease in hand dexterity. This trial is registered with Clinical Trial Registry no. ChiCTR1900025358.

4 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.

8,730 citations

01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

4,408 citations

Journal ArticleDOI
TL;DR: In this article, the authors describe the long-term health consequences of patients with COVID-19 who have been discharged from hospital and investigate the associated risk factors, in particular disease severity.

2,933 citations

Journal ArticleDOI
07 Apr 2020-BMJ
TL;DR: Proposed models for covid-19 are poorly reported, at high risk of bias, and their reported performance is probably optimistic, according to a review of published and preprint reports.
Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

2,183 citations