Mrs Shelda Sajeev

  • 15 Citations
  • 2 h-Index
20152019

Research output per year

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Personal profile

Research Biography

Shelda is a Research Associate at Flinders Digital Health Research Centre. Shelda is the project lead of one of the Shandong Flinders Joint project (Machine Learning for improving Heart Disease Risk Prediction) and principal investigator of AI €“ PREDICT (The utility of health data and predictive analytics in developing prognostic cardiac event and mortality models for patients presenting to the emergency department) project. Her research expertise in pattern recognition, image processing, medical image analysis, data analysis and machine learning.

Research Interests

Medical Image Analysis, Data Analysis, Mammography, Image processing, Pattern recognition, Computer vision, Artifical Intelligence

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Research Output

  • 15 Citations
  • 2 h-Index
  • 4 Paper
  • 2 Conference contribution
  • 1 Article

Cardiovascular Risk Prediction Models: A Scoping Review

Sajeev, S. & Maeder, A. J., 29 Jan 2019, p. 1-5. 5 p.

Research output: Contribution to conferencePaper

  • 1 Citation (Scopus)

    Deep Learning to Improve Heart Disease Risk Prediction

    Sajeev, S., Maeder, A., Champion, S., Beleigoli, A., Ton, C., Kong, X. & Shu, M., 1 Jan 2019, Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings. Liao, H., Wang, G., Liu, Y., Ding, Z., Balocco, S., Zhang, F., Duong, L., Phellan, R., Zahnd, G., Albarqouni, S., Demirci, S., Breininger, K., Moriconi, S. & Lee, S-L. (eds.). Springer, p. 96-103 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11794 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Graph Modeling for Identifying Breast Tumor Located in Dense Background of a Mammogram

    Sajeev, S., Bajger, M. & Lee, G., 2019, Graph Learning in Medical Imaging: First International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Zhang, D., Zhou, L., Jie, B. & Liu, M. (eds.). Switzerland: Springer, p. 147-154 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11849 LNCS).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • 1 Citation (Scopus)

    Superpixel texture analysis for classification of breast masses in dense background

    Sajeev, S., Bajger, M. & Lee, G., 2018, In : IET Computer Vision. 12, 6, p. 779-786 8 p.

    Research output: Contribution to journalArticle

  • 1 Citation (Scopus)

    Structured Micro-Pattern Based LBP Features for Classification of Masses in Dense Breasts

    Sajeev, S., Bajger, M. & Lee, G., 2017, p. 1-8. 8 p.

    Research output: Contribution to conferencePaper

  • 2 Citations (Scopus)