He, Y. orcid.org/0000-0003-3464-7526, Cooney, C.R. orcid.org/0000-0002-4872-9146, Maddock, S. et al. (1 more author) (2025) PhenoLearn: a user-friendly toolkit for image annotation and deep learning-based phenotyping for biological datasets. Journal of Evolutionary Biology. voaf058. ISSN 1010-061X
Abstract
The digitisation of natural history specimens has unlocked opportunities for large-scale phenotypic trait analysis. In recent years, deep learning has shown significant results in accurately predicting annotations on 2D specimen photographs. However, it can be challenging for biologists without extensive related expertise to easily use deep learning. Here, we introduce PhenoLearn, a toolkit developed for biologists to generate annotations on 2D specimen images using deep learning. PhenoLearn integrates graphical user interfaces (GUIs) within its two main modules, PhenoLabel for image annotation and PhenoTrain for model training and prediction. GUIs increase accessibility and reduce the need for computational expertise, allowing biologists to intuitively go through a workflow of labelling training sets, using deep learning, and reviewing predictions in the same tool. We demonstrate PhenoLearn's capabilities through a case study involving the segmentation of plumage areas on bird images, showcasing prediction accuracy and the running time with and without GPU, highlighting its potential to generate annotations with minimal computational cost and time. The toolkit's modular design and flexibility ensure adaptability, allowing for integration with other tools amidst rapidly evolving deep learning approaches. PhenoLearn bridges the gap between specimen digitisation and downstream analysis, providing biologists with broader access to deep learning. The source code, installation guides, tutorials with screenshots, and a small demo dataset for PhenoLearn can be found at https://github.com/echanhe/phenolearn.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2025. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Zoology; Ecology; Evolutionary Biology; Biological Sciences; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; Bioengineering; Generic health relevance |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) |
Funding Information: | Funder Grant number NATURAL ENVIRONMENT RESEARCH COUNCIL NE/T01105X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 May 2025 08:51 |
Last Modified: | 25 May 2025 07:50 |
Status: | Published online |
Publisher: | Oxford University Press (OUP) |
Refereed: | Yes |
Identification Number: | 10.1093/jeb/voaf058 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:227048 |