• Source: Scopus
  • Calculated based on no. of publications stored in Pure and citations from Scopus
20152020

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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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  • Mammographic mass identification in dense breasts using multi-scale analysis of structured micro-patterns

    Sajeev, S., Bajger, M., Lee, G., Muramatsu, C. & Fujita, H., 2020, 15th International Workshop on Breast Imaging, IWBI 2020. Bosmans, H., Marshall, N. & Van Ongeval, C. (eds.). Washington, USA: SPIE, 6 p. 1151323. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11513).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Cardiovascular Risk Prediction Models: A Scoping Review

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

    Research output: Contribution to conferencePaperpeer-review

    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 contributionpeer-review

  • 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 contributionpeer-review

    1 Citation (Scopus)
  • Superpixel pattern graphs for identifying breast mass ROIs in dense background: a preliminary study

    Sajeev, S., Bajger, M. & Lee, G., 2018, 14th International Workshop on Breast Imaging (IWBI 2018). Krupinski, E. A. (ed.). Washington, USA: SPIE, Vol. 10718. 10 p. 107180V. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; vol. 10718).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Citations (Scopus)
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