Bin Zheng

Bin Zheng
University of Oklahoma | ou · School of Electrical and Computer Engineering

PhD

About

451
Publications
50,497
Reads
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8,116
Citations
Citations since 2017
166 Research Items
4776 Citations
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201720182019202020212022202302004006008001,000
201720182019202020212022202302004006008001,000
Introduction
I am currently working in School of Electrical and Computer Engineering and Stephenson Cancer Center, University of Oklahoma. I teach Optical Engineering courses and do research in medical imaging. The focus of my Computer-aided diagnosis lab at OU advanced cancer imaging core facility is to develop and validate computerized image biomarkers for improving cancer diagnosis and prognosis assessment. The detailed research activities can be found at website: http://faculty-staff.ou.edu/Z/Bin.Zheng-1
Additional affiliations
August 2013 - present
University of Oklahoma
Position
  • Professor (Full)
Description
  • Optical Engineering, Optical Information Processing
May 2013 - present
University of Oklahoma
Position
  • Professor (Full)
Description
  • My research focuses on developing and evaluating medical imaging and signal detection technologies, as well as computerized image phenotype biomarkers for improving cancer diagnosis and prognosis assessment.
January 2009 - May 2013
University of Pittsburgh
Position
  • Professor (Full)
Education
August 1989 - May 1993
University of Delaware
Field of study
  • Electrical Engineering

Publications

Publications (451)
Preprint
Full-text available
Deep Convolutional Neural Networks (DCNNs) have emerged as powerful components of computer-aided systems to detect and classify diseases in medical images. In order to robustly achieve high performance, a large and diverse dataset with annotated images is required to train or fine-tune DCNNs. However, such datasets are often very difficult and/or e...
Article
Full-text available
Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention me...
Article
Background A new modality, phase-sensitive breast tomosynthesis (PBT), may have similar diagnostic performance to conventional breast tomosynthesis but with a reduced radiation dose. Purpose To perform a pilot study of the performance of a novel PBT system compared with conventional digital breast tomosynthesis (DBT) in patients undergoing addition...
Article
Full-text available
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular...
Article
Full-text available
Stress-induced hyperglycemia (SIH) is a neuroendocrine response to acute illness. Although SIH has an adverse association with intracerebral hemorrhage (ICH), quantitative measures and determinants of SIH are not well delineated. In the present study, we objectively evaluated SIH using glycemic gap (GG) and identified its radiological and clinical...
Article
Full-text available
Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operations, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multip...
Preprint
Full-text available
Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multipl...
Preprint
Full-text available
Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying breast cancer from unregistered multi-view mammograms. This motivates us to levera...
Preprint
Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multipl...
Article
Full-text available
Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large a...
Preprint
Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse ret...
Article
Background Occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients remains a major diagnostic challenge. The aim of this study was to develop novel predictive models for identification of OPM in AGCs. Method A total of 810 patients with primary AGCs from two hospitals were retrospectively selected and divided into training (n...
Article
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis...
Article
Background: Endovascular mechanical thrombectomy (EMT) is an effective method to treat acute ischemic stroke (AIS) patients due to large vessel occlusion (LVO). However, stratifying AIS patients who can and cannot benefit from EMT remains a clinical challenge. Objective: To develop a new quantitative image marker computed from pre-intervention c...
Article
Accurately predicting clinical outcome of aneurysmal subarachnoid hemorrhage (aSAH) patients is difficult. The purpose of this study was to develop and test a new fully-automated computer-aided detection (CAD) scheme of brain computed tomography (CT) images to predict prognosis of aSAH patients. A retrospective dataset of 59 aSAH patients was assem...
Article
Significance: Searching analyzable metaphase chromosomes is a critical step for the diagnosis and treatment of leukemia patients, and the searching efficiency is limited by the difficulty that the conventional microscopic systems have in simultaneously achieving high resolution and a large field of view (FOV). However, this challenge can be addres...
Preprint
Full-text available
This study aims to develop a novel computer-aided diagnosis (CAD) scheme for mammographic breast mass classification using semi-supervised learning. Although supervised deep learning has achieved huge success across various medical image analysis tasks, its success relies on large amounts of high-quality annotations, which can be challenging to acq...
Article
Purpose: To compare imaging performance of a cadmium telluride (CdTe) based photon counting detector (PCD) with a CMOS based energy integrating detector (EID) for potential phase sensitive imaging of breast cancer. Methods: A high energy inline phase sensitive imaging prototype consisting of a microfocus X-ray source with geometric magnification...
Preprint
Full-text available
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis...
Article
Phase-sensitive x-ray imaging continues to attract research for its ability to visualize weakly absorbing details like those often encountered in biology and medicine. We have developed and assembled the first inline-based high-energy phase sensitive breast tomosynthesis (PBT) system, which is currently undergoing patient imaging testing at a clini...
Article
Full-text available
Introduction Hypoxia-inducible factor (HIF)1α has been shown to be activated and induces a glycolytic shift under hypoxic condition, however, little attention was paid to the role of HIF1α-actuated fructolysis in hypoxia-induced heart injury. Objectives In this study, we aim to explore the molecular mechanisms of miR-155-mediated fructose metaboli...
Article
Objective: Based on the hypothesis that adding a cross-modal and cross-attention (C2MA) mechanism into a deep learning network improves accuracy and efficacy of medical image segmentation, we propose to test a novel network to segment acute ischemic stroke (AIS) lesions from four CT perfusion (CTP) maps. Methods: The proposed network uses a C2MA...
Article
Chronic angiotensin II (Ang II) stimulation induces vascular smooth muscle cell (VSMC) senescence, and circRNAs and members of the ILF3 family are implicated in cellular senescence, but the mechanism underlying regulation of circRNAs and ILF3 by Ang II in VSMCs remains poorly understood. Here, a model of Ang II-induced VSMC senescence and the renal...
Article
Purpose Increasing measures of adiposity have been correlated with poor oncologic outcomes and a lack of response to anti-angiogenic therapies. Limited data exists on the impact of subcutaneous fat density (SFD) and visceral fat density (VFD) on oncologic outcomes. This ancillary analysis of GOG-218, evaluates whether imaging markers of adiposity w...
Conference Paper
The purpose of this study is to develop a machine learning model with the optimal features computed from mammograms to classify suspicious regions as benign and malignant. To this aim, we investigate the benefits of implementing a machine learning approach embedded with a random projection algorithm to generate an optimal feature vector and improve...
Article
Full-text available
Endothelial dysfunction and diabetic vascular disease induced by chronic hyperglycemia involve complex interactions among high glucose, long non‑coding RNAs (lncRNAs), microRNAs (miRNAs or miRs) and the Ser/Thr kinase AKT. However, the molecular mechanisms underlying the regulatory crosstalk between these have not yet been completely elucidated. Th...
Article
Full-text available
Background: This article reports the first x-ray phase sensitive breast tomosynthesis (PBT) system that is aimed for direct translation to clinical practice for the diagnosis of breast cancer. Purpose: To report the preclinical evaluation and comparison of the newly built PBT system with a conventional digital breast tomosynthesis (DBT) system....
Article
formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> $Objective:$ Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the...
Article
Background and Objective Non-invasively predicting the risk of cancer metastasis before surgery can play an essential role in determining which patients can benefit from neoadjuvant chemotherapy. This study aims to investigate and test the advantages of applying a random projection algorithm to develop and optimize a radiomics-based machine learnin...
Article
Introduction Preoperative diagnosis of No.10 lymph nodes (LNs) metastases in advanced proximal gastric cancer (APGC) patients remains a challenge. The aim of this study was to develop a CT-based radiomics nomogram for identification of No.10 LNs status in APGCs. Materials and methods A total of 515 patients with primary APGCs were retrospectively...
Article
Objective This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia. Method CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bi...
Article
Background and Objective In diagnosis of cervical cancer patients, lymph node (LN) metastasis is a highly important indicator for the following treatment management. Although CT/PET (i.e., computed tomography/positron emission tomography) examination is the most effective approach for this detection, it is limited by the high cost and low accessibi...
Preprint
Full-text available
Machine learning is widely used in developing computer-aided diagnosis (CAD) schemes of medical images. However, CAD usually computes large number of image features from the targeted regions, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models. In this study, we investigate feasibi...
Preprint
Full-text available
Background and Objective: Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods for cancer patients (including who can benefit from neoadjuvant chemotherapy). Although developing radiomics based machine learning (ML) models has attracted broad research interest for th...
Article
Background and objective: The deep neural network model can learn complex non-linear relationships in the data and has superior flexibility and adaptability. A downside of this flexibility is that they are sensitive to initial conditions, both in terms of the initial random weights and in terms of the statistical noise in the training dataset. And...
Article
Objective: The objective of this study is to demonstrate the potential of utilizing mid-energy x-rays for in-line phase-sensitive breast cancer imaging by phantom studies. Methods: The midenergy (50-80kV) in-line phase sensitive imaging prototype was used to acquire images of the contrast-detail mammography (CDMAM) phantom, an ACR accreditation...
Preprint
Objective: This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia. Method: CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a...
Article
Full-text available
The inflammation and proliferation of vascular smooth muscle cells (VSMCs) are the basic pathological feature of proliferative vascular diseases. Tanshinone ⅡA (Tan ⅡA), which is the most abundant fat-soluble element extracted from Salvia miltiorrhiza, has potent protective effects on the cardiovascular system. However, the underlying mechanism is...
Article
The aim of this study was to develop a general and automatic recognition framework for recognising the daily behaviours of lactating sows to save manual labour and promote smart management. The proposed framework used both image analysis techniques in still image and motion analysis techniques in spatiotemporal videos to recognise sow drinking, fee...
Article
This paper proposes an end-to-end refined two-stream RGB-D Faster region convolutional neural network (R-CNN) algorithm, which fuses RGB-D image features in the feature extraction stage for recognising five postures of lactating sows (standing, sitting, sternal recumbency, ventral recumbency, and lateral recumbency) in scenes at a pig farm. Based o...
Article
Objective: To develop a deep learning-based artificial intelligence (AI) scheme for predicting the likelihood of the ground-glass nodule (GGN) detected on CT images being invasive adenocarcinoma (IA) and also compare the accuracy of this AI scheme with that of two radiologists. Methods: First, we retrospectively collected 828 histopathologically...
Article
Full-text available
Abstract In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes...
Article
Objective: Investigate predictive factors and develop outcome assessment tool to determine clinical outcome after endovascular mechanical thrombectomy (EMT) in patient presenting with large vessel occlusion (LVO). Methods: A retrospective analysis of a prospective cohort of patients who underwent EMT after adoption of expanded time window up to...
Article
This study aims to develop and evaluate a new computer-aided diagnosis (CADx) scheme based on analysis of global mammographic image features to predict likelihood of cases being malignant. An image dataset involving 1,959 cases was retrospectively assembled. Suspicious lesions were detected and biopsied in each case. Among them, 737 cases are malig...
Article
Full-field digital mammography (FFDM) and magnetic resonance imaging (MRI) are gold-standard techniques for breast cancer detection. The newly contrast-enhanced digital mammography (CEDM) integrates the complementary strengths of FFDM and MRI, and is being incorporated into the practice of leading institutions. The current clinical practice using C...
Article
Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of...
Article
Full-text available
Objective: To examine associations of body mass index (BMI), subcutaneous fat area (SFA) and density (SFD), visceral fat area (VFA) and density (VFD) and total psoas area (TPA) to outcomes among patients receiving chemotherapy with or without bevacizumab for advanced or recurrent endometrial cancer (EC). Methods: This was a multi-institutional,...
Article
Objective: Adiposity has been hypothesized to interfere with the activity of bevacizumab (BEV), an anti-angiogenic agent. Measurements of adiposity, BMI, surface fat area (SFA), and visceral fat area (VFA) were investigated as prognostic of oncologic outcomes among patients treated with chemotherapy, with or without BEV, on GOG 218, a prospective...
Article
Background and objective: This study aims to develop and evaluate a unique global mammographic image feature analysis scheme to predict likelihood of a case depicting the detected suspicious breast mass being malignant for breast cancer. Methods: From the entire breast area depicting on the mammograms, 59 features were initially computed to char...
Article
Full-text available
Acute phase after aneurysmal subarachnoid hemorrhage (aSAH) is associated with several metabolic derangements including stress-induced hyperglycemia (SIH). The present study is designed to identify objective radiological determinants for SIH to better understand its contributory role in clinical outcomes after aSAH. A computer-aided detection tool...