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Yuchen Qiu
Researcher at University of Oklahoma
Publications - 77
Citations - 1355
Yuchen Qiu is an academic researcher from University of Oklahoma. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 14, co-authored 58 publications receiving 806 citations.
Papers
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Journal ArticleDOI
Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.
Morteza Heidari,Seyedehnafiseh Mirniaharikandehei,Abolfazl Zargari Khuzani,Gopichandh Danala,Yuchen Qiu,Bin Zheng +5 more
TL;DR: It is demonstrated that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.
Journal ArticleDOI
Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients
TL;DR: A new approach has potential to assist clinicians in more effectively managing stage I NSCLC patients to reduce cancer recurrence risk and yielded significantly higher discriminatory power than a genomic biomarker-based classifier.
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A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images
TL;DR: The feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results is demonstrated.
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Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.
Morteza Heidari,Abolfazl Zargari Khuzani,Alan B. Hollingsworth,Gopichandh Danala,Seyedehnafiseh Mirniaharikandehei,Yuchen Qiu,Hong Liu,Bin Zheng +7 more
TL;DR: This study demonstrated that applying the LPP algorithm effectively reduced feature dimensionality, and yielded higher and potentially more robust performance in predicting short-term breast cancer risk.
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A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology
TL;DR: This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.