Workshops and courses

ELIXIR node event Bioimaging data analysis: from zero to machine learning hero

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Date: 19 - 21 October 2026

Language of instruction: English

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Bioimaging Data Analysis: From Zero to Machine Learning Hero is a three‑day, project‑style course that guides participants through the full journey of understanding, exploring, and analyzing bioimaging data. Beginning with common pitfalls in image acquisition and an accessible introduction to machine learning, participants progressively build practical skills using powerful open‑source tools. Hands‑on sessions with Napari, QuPath, ilastik, and Cellpose provide an interactive foundation for image visualization, segmentation, and quantitative analysis, while lunch‑and‑learn moments introduce broader concepts such as emerging machine‑learning approaches and the Bioimaging Model Zoo.In the final stretch, participants dive into state‑of‑the‑art deep‑learning tools, including SAM, Empanada, and Napari‑based neural network workflows, before shifting perspective toward responsible and reproducible science. Day three focuses on image ethics, FAIR data principles, and personalized project work in a “Bring Your Own Data” session—allowing each participant to apply new skills directly to their own research questions and leave the course with practical, actionable experience.This is an event organized as 'RACE event', work package 3 of the RACE project funded by the European Union under Horizon Europe (Project no 101059801). Read more about RACE at https://www.iimcb.gov.pl/en/race/

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Funded by the European Union. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of theEuropean Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Keywords: basic bioinformatics, imaging

Venue: IIMCB - International Institute of Molecular and Cell Biology in Warsaw, Hankiewicza 2

City: Warsaw

Country: Poland

Postcode: 02-103

Learning objectives:

  • “Apply pixel and object classification workflows in QuPath and interpret the resulting segmentation outputs for biological relevance”
  • “Compare classical image analysis approaches with machine learning–based workflows selecting appropriate methods for different bioimaging tasks”
  • “Design and implement a basic analysis pipeline for their own dataset (“Bring Your Own Data”) demonstrating problem solving tool selection and critical interpretation of results”
  • “Discuss best practices in image ethics and apply FAIR data principles when preparing imaging data for sharing publication or reuse”
  • “Execute deep learning based segmentation tools such as SAM and Empanada and assess their performance using appropriate evaluation criteria”
  • “Explain common pitfalls in bioimage acquisition and evaluate how they affect downstream analysis quality”
  • “Operate core functionalities of Napari QuPath ilastik and Cellpose to visualize segment and quantify biological images in a reproducible manner”

Organizer: VIB

Event types:

  • Workshops and courses

Sponsors: RACE

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