16th JPIAMR transnational call for research projects - AMR diagnostics and surveillance 2023 (DISTOMOS)

General Information

• Type: Partner looking for project
• Organisation: Jagiellonian University
• Country: Poland (PL)
• Career stage: Other.

Research area

• Scientific area(s) of the call:
  1. Topic 1: to develop novel, or improve existing, diagnostics, including point of care diagnostics, that can rule out antimicrobial use or help identify the most effective antimicrobial treatment
• One Health Setting:

  Human Health

  Animal Health (incl. wild-life, livestock, acquatic organisms, and companion animals)
  Plants (incl. trees and crops)

• Keywords:

  biomedical image analysis; explainable artificial intelligence; antibiotic resistance; diagnostic; bacteria;

• Brief description of your expertise / expertise you are looking for:

  We are a team of machine learning and microbiology researchers experienced in developing new diagnostic methods based on pathogens’ microscopy images and artificial intelligence algorithms. We introduce novel cutting-edge deep learning methodologies considering their transparency together with ethical aspects. Our microbiological experience is focused on human pathogens, their resistance profiles, virulence factors, molecular detection and typing, biofilm analysis, anti-bacterial surface for medical application, metagenomics, microscopy imaging. We regularly publish on machine learning conferences and top-tier journals such as: NeurIPS, ECCV, IJCAI, KDD, IEEE Journal of Biomedical and Health Informatics , Scientific Reports, Frontiers in Microbiology, BMC Microbiology. Some of the publications: Zieliński, B., Plichta, A., Misztal, K., Spurek, P., Brzychczy-Włoch, M., & Ochońska, D. (2017). Deep learning approach to bacterial colony classification. PloS one, 12(9), e0184554. Zieliński, B., Sroka-Oleksiak, A., Rymarczyk, D., Piekarczyk, A., & Brzychczy-Włoch, M. (2020). Deep learning approach to describe and classify fungi microscopic images. PloS one, 15(6), e0234806. Borowa, A., Rymarczyk, D., Ochońska, D., Brzychczy-Włoch, M., & Zieliński, B. (2021, July). Deep learning classification of bacteria clones explained by persistence homology. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. Borowa, A., Rymarczyk, D., Ochońska, D., Sroka-Oleksiak, A., Brzychczy-Włoch, M., & Zieliński, B. (2022). Identifying bacteria species on microscopic polyculture images using deep learning. IEEE Journal of Biomedical and Health Informatics.

• Brief description of your project / the project you would like to join:

  We are interested in establishing cooperation on a project using multiple data sources, including biomedical imaging data, to study the pathogens\' resistance. We can contribute with our rich experience and unique skills in the application of artificial intelligence methods into microbiological image analysis and pattern recognition.

Bartosz Zieliński

• Organisation: Jagiellonian University
• Position: Associate professor
• E-mail: bartosz.zielinski@uj.edu.pl
• Web: bartoszzielinski.github.io/