Artificial Intelligence for Single-Cell Analysis: Detection, Segmentation, Classification, and Tracking

Description:
This workshop introduces applications of Artificial Intelligence (AI) in microfluidics and biomedical imaging, with a focus on detecting, classifying, and segmenting single cells and microscopic objects using machine learning models. Participants will gain hands-on experience across the full workflow, from data preparation and model training to evaluation and result interpretation. Following a brief conceptual overview, attendees will complete two guided practical exercises using pre-configured environments on Google Colab and Kaggle. These exercises will explore real-time object detection and image classification using biomedical datasets and microscopy images. The workshop combines theory with practical skills development, equipping participants with the tools and confidence to apply AI techniques in their own single-cell research.

Objectives:
By the end of the workshop, participants will:

• Understand the fundamentals of AI and, more specifically, deep learning and commonly used neural network architectures.
• Learn how to apply these models to biomedical and microfluidic imaging tasks, such as object detection, classification, and segmentation.
• Gain hands-on experience using Google Colab and Kaggle notebooks, simulating a local coding environment for running and executing real-world deep learning workflows.