Speaker
Description
Quantitative analysis of biological phenomena is practically a requirement in contemporary research, particularly when dealing with image data. AI-assisted tools simplify complex tasks like image segmentation, even for those without computational expertise. Supervised machine learning excels in classifying data from minimal manual annotations. While several software solutions exist for timelapse data analysis, they are often domain-specific and limited in scope.
To generalize Python-based image analysis, napari offers a flexible plugin engine. We recently presented napari-signal-selector, a plugin for interactive annotation of temporal features connected to image data. Building on this, we now introduce napari-signal-classifier, a plugin leveraging user annotations to classify signals using a Random Forest Classifier applied to a large set of signal features calculated by tsfresh. For shorter event classification, it integrates classical template matching for detection followed by subsequent event classification.
This innovative tool empowers researchers to guide AI with their expertise, enhancing the accuracy and relevance of signal classification in biological data analysis.
| Authors | Marcelo Leomil Zoccoler* |
|---|---|
| Keywords | signal classification, event detection, machine learning, timelapse, AI-assisted tools |
