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Related Experiment Video

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PhenoLearn: a user-friendly toolkit for image annotation and deep learning-based phenotyping for biological datasets.

Yichen He1,2, Christopher R Cooney1, Steve Maddock3

  • 1Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, United Kingdom.

Journal of Evolutionary Biology
|May 14, 2025
PubMed
Summary

PhenoLearn is a new toolkit that makes deep learning accessible for biologists to analyze 2D specimen images. It simplifies generating annotations for phenotypic trait analysis, reducing computational costs and time.

Keywords:
deep learningimage annotationphenotypic traitphenotypingtoolkit with user interface

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Area of Science:

  • * Biodiversity Informatics
  • * Computational Biology
  • * Digital Morphology

Background:

  • * Specimen digitization enables large-scale phenotypic trait analysis.
  • * Deep learning models accurately predict annotations on 2D specimen images.
  • * Biologists often lack the computational expertise to utilize deep learning tools.

Purpose of the Study:

  • * Introduce PhenoLearn, a biologist-friendly toolkit for deep learning-based annotation of 2D specimen images.
  • * Enhance accessibility of deep learning for phenotypic trait analysis in natural history.
  • * Streamline the workflow for image annotation, model training, and prediction.

Main Methods:

  • * PhenoLearn integrates graphical user interfaces (GUIs) for intuitive use.
  • * Two core modules: PhenoLabel for image annotation and PhenoTrain for model training/prediction.
  • * Case study: Segmentation of bird plumage areas to demonstrate capabilities.

Main Results:

  • * PhenoLearn successfully segmented plumage areas in bird images.
  • * Demonstrated prediction accuracy and efficiency (with/without GPU).
  • * Highlighted minimal computational cost and time for generating annotations.

Conclusions:

  • * PhenoLearn bridges the gap between specimen digitization and deep learning analysis.
  • * Increases biologist access to advanced computational tools for trait analysis.
  • * Offers a modular and adaptable solution for evolving deep learning approaches.