Abstract

Image annotations and model training are essential in bioimage analysis tasks. In biology and related fields, researchers often deal with large volumes of microscopy images that require accurate annotation to train machine learning models. Automate this process can save time and improve reproducibility, but high-quality training data remains critical. Human-in-the-loop workflows have emerged as a solution to bridge the gap between manual annotation and automated model training, enabling iterative improvements through user interaction.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

• Why use YOLO for object detection and segmentation in bioimage analysis?
• How can I train and use a custom YOLO model for detection or segmentation tasks using Galaxy?

Learning Objectives

• Preprocess images (e.g., histogram equalization, format conversion) to prepare data for annotation and training
• Perform human-in-the-loop object annotation using AnyLabeling interactive tool.
• Convert AnyLabeling annotation files into YOLO compatible format for training.
• Train a custom YOLO model.