Research Incubator: Artificial Intelligence in Health - SemillIAS

Organization profile

Organization profile

The seedbed of artificial intelligence in health seeks to generate spaces for discussion, where from the problems that can be found in the areas of medicine and health sciences, and possible solutions from the engineering area can be merged to find viable solutions. At the same time, through the seedbed seeks to inform students and professionals in the area of artificial intelligence that is increasingly used in different fields, being considered transversal for many sciences.

Research Lines:

Mainly, but not limited to:

  • Analysis of physiological phenomena through the use of biosignal processing.
  • Motion analysis from pose models.
  • Bioinformatics and Artificial Intelligence (in conjunction with SyNERGIA).
  • Health Data Science.
  • Diagnostic Support Systems.
  • Machine and Deep Learning.
  • Natural Language Processing.

Objectives:

  1. To encourage the study of existing techniques in artificial intelligence with the purpose of applying them in the solution of problems in the field of health.
  2. Establish solutions in the area of medicine and health sciences through different paradigms of artificial intelligence.
  3. To generate new alternatives for the creation of artificial intelligence models based on bio-inspired systems.
  4. To establish spaces for meeting different disciplines for joint interdisciplinary work in solution to health problems.

Organization profile

The seedbed of artificial intelligence in health seeks to generate spaces for discussion, where from the problems that can be found in the areas of medicine and health sciences, and possible solutions from the engineering area can be merged to find viable solutions. At the same time, through the seedbed seeks to inform students and professionals in the area of artificial intelligence that is increasingly used in different fields, being considered transversal for many sciences.

Research Lines:

Mainly, but not limited to:

  • Analysis of physiological phenomena through the use of biosignal processing.
  • Motion analysis from pose models.
  • Bioinformatics and Artificial Intelligence (in conjunction with SyNERGIA).
  • Health Data Science.
  • Diagnostic Support Systems.
  • Machine and Deep Learning.
  • Natural Language Processing.

Objectives:

  1. To encourage the study of existing techniques in artificial intelligence with the purpose of applying them in the solution of problems in the field of health.
  2. Establish solutions in the area of medicine and health sciences through different paradigms of artificial intelligence.
  3. To generate new alternatives for the creation of artificial intelligence models based on bio-inspired systems.
  4. To establish spaces for meeting different disciplines for joint interdisciplinary work in solution to health problems.

Fingerprint

Dive into the research topics where Research Incubator: Artificial Intelligence in Health - SemillIAS is active. These topic labels come from the works of this organization's members. Together they form a unique fingerprint.

Collaborations and top research areas from the last five years

Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
  • A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement

    Pérez, A. D., Perdomo, O., Rios, H., Rodríguez, F. & González, F. A., Nov 20 2020, Ophthalmic Medical Image Analysis - 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Proceedings. Fu, H., Garvin, M. K., MacGillivray, T., Xu, Y. & Zheng, Y. (eds.). Springer Science and Business Media Deutschland GmbH, Vol. 12069. p. 185-194 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12069 LNCS).

    Research output: Chapter in Book/ReportChapter (peer-reviewed)peer-review

    11 Scopus citations
  • A lightweight deep learning model for mobile eye fundus image quality assessment

    Pérez, A. D., Perdomo Charry, O. J. & González, F. A., Jan 3 2020, 15th International Symposium on Medical Information Processing and Analysis. Romero, E., Lepore, N. & Brieva, J. (eds.). SPIE, 113300K. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11330).

    Research output: Chapter in Book/ReportChapter (peer-reviewed)peer-review

    12 Scopus citations
  • Automatic estimation of pose and falls in videos using computer vision model

    Calvache Briceño, D. A., Bernal, H. A., Guarín, J. F., Aguía, K., Orjuela-Cañón, A. D. & Perdomo, O. J., Nov 3 2020, 16th International Symposium on Medical Information Processing and Analysis. Romero, E., Lepore, N., Brieva, J. & Linguraru, M. (eds.). SPIE, 115830W. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11583).

    Research output: Chapter in Book/ReportChapter (peer-reviewed)peer-review

    4 Scopus citations