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Connection

Bin Zheng to Algorithms

This is a "connection" page, showing publications Bin Zheng has written about Algorithms.
Connection Strength

3.262
  1. Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms. Int J Med Inform. 2020 12; 144:104284.
    View in: PubMed
    Score: 0.577
  2. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm. Phys Med Biol. 2018 01 30; 63(3):035020.
    View in: PubMed
    Score: 0.480
  3. Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions. Phys Med Biol. 2017 01 21; 62(2):358-376.
    View in: PubMed
    Score: 0.445
  4. A new approach to develop computer-aided detection schemes of digital mammograms. Phys Med Biol. 2015 Jun 07; 60(11):4413-27.
    View in: PubMed
    Score: 0.398
  5. Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme. Phys Med Biol. 2014 Aug 07; 59(15):4357-73.
    View in: PubMed
    Score: 0.376
  6. Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images. Comput Methods Programs Biomed. 2021 Mar; 200:105937.
    View in: PubMed
    Score: 0.147
  7. Computer-aided classification of mammographic masses using visually sensitive image features. J Xray Sci Technol. 2017; 25(1):171-186.
    View in: PubMed
    Score: 0.111
  8. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. J Xray Sci Technol. 2017; 25(5):751-763.
    View in: PubMed
    Score: 0.111
  9. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy. Med Phys. 2015 Nov; 42(11):6520-8.
    View in: PubMed
    Score: 0.103
  10. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme. Med Phys. 2014 Aug; 41(8):081906.
    View in: PubMed
    Score: 0.094
  11. Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model. Int J Comput Assist Radiol Surg. 2014 Nov; 9(6):1005-20.
    View in: PubMed
    Score: 0.092
  12. A rule-based computer scheme for centromere identification and polarity assignment of metaphase chromosomes. Comput Methods Programs Biomed. 2008 Jan; 89(1):33-42.
    View in: PubMed
    Score: 0.060
  13. Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. J Biomed Inform. 2008 Apr; 41(2):264-71.
    View in: PubMed
    Score: 0.058
  14. A computer-aided method to expedite the evaluation of prognosis for childhood acute lymphoblastic leukemia. Technol Cancer Res Treat. 2006 Aug; 5(4):429-36.
    View in: PubMed
    Score: 0.054
  15. Recent advances and clinical applications of deep learning in medical image analysis. Med Image Anal. 2022 07; 79:102444.
    View in: PubMed
    Score: 0.040
  16. Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer. Comput Methods Programs Biomed. 2019 Oct; 179:104995.
    View in: PubMed
    Score: 0.033
  17. Detectability comparison of simulated tumors in digital breast tomosynthesis using high-energy X-ray inline phase sensitive and commercial imaging systems. Phys Med. 2018 Mar; 47:34-41.
    View in: PubMed
    Score: 0.030
  18. Impact of the optical depth of field on cytogenetic image quality. J Biomed Opt. 2012 Sep; 17(9):96017-1.
    View in: PubMed
    Score: 0.021
  19. Automated detection and analysis of fluorescent in situ hybridization spots depicted in digital microscopic images of Pap-smear specimens. J Biomed Opt. 2009 Mar-Apr; 14(2):021002.
    View in: PubMed
    Score: 0.016
  20. Automated classification of metaphase chromosomes: optimization of an adaptive computerized scheme. J Biomed Inform. 2009 Feb; 42(1):22-31.
    View in: PubMed
    Score: 0.015
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.

THIS IS A DEVELOPMENT VERSION OF PROFILES. PLEASE GO TO THE PRODUCTION ENVIRONMENT FOR UPDATES