FeatureForest: the power of foundation models, the usability of random forests
Abstract Analysis of biological images relies heavily on segmenting the biological objects of interest in the image before performing quantitative analysis. Deep learning (DL) is ubiquitous in such segmentation tasks, but can be cumbersome to apply, as it often requires a large amount of manual labe...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Imaging |
| Online Access: | https://doi.org/10.1038/s44303-025-00089-9 |
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| author | Mehdi Seifi Damian Dalle Nogare Juan Manuel Battagliotti Vera Galinova Ananya Kedige Rao Pierre-Henri Jouneau Anwai Archit AI4Life Horizon Europe Programme Consortium Constantin Pape Johan Decelle Florian Jug Joran Deschamps |
| author_facet | Mehdi Seifi Damian Dalle Nogare Juan Manuel Battagliotti Vera Galinova Ananya Kedige Rao Pierre-Henri Jouneau Anwai Archit AI4Life Horizon Europe Programme Consortium Constantin Pape Johan Decelle Florian Jug Joran Deschamps |
| author_sort | Mehdi Seifi |
| collection | DOAJ |
| description | Abstract Analysis of biological images relies heavily on segmenting the biological objects of interest in the image before performing quantitative analysis. Deep learning (DL) is ubiquitous in such segmentation tasks, but can be cumbersome to apply, as it often requires a large amount of manual labeling to produce ground-truth data, and expert knowledge to train the models. More recently, large foundation models, such as SAM, have shown promising results on scientific images. They, however, require manual prompting for each object or tedious post-processing to selectively segment these objects. Here, we present FeatureForest, a method that leverages the feature embeddings of large foundation models to train a random forest classifier, thereby providing users with a rapid way of semantically segmenting complex images using only a few labeling strokes. We demonstrate the improvement in performance over a variety of datasets and provide an open-source implementation in napari that can be extended to new models. |
| format | Article |
| id | doaj-art-e227485bc4d747268c624fd78bd559ea |
| institution | Kabale University |
| issn | 2948-197X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Imaging |
| spelling | doaj-art-e227485bc4d747268c624fd78bd559ea2025-08-20T04:02:55ZengNature Portfolionpj Imaging2948-197X2025-07-013111210.1038/s44303-025-00089-9FeatureForest: the power of foundation models, the usability of random forestsMehdi Seifi0Damian Dalle Nogare1Juan Manuel Battagliotti2Vera Galinova3Ananya Kedige Rao4Pierre-Henri Jouneau5Anwai Archit6AI4Life Horizon Europe Programme ConsortiumConstantin Pape7Johan Decelle8Florian Jug9Joran Deschamps10Computational Biology Research Center, Human TechnopoleBioimage Analysis Unit, National Facility for Data Handling and Analysis, Human TechnopoleBioimage Analysis Unit, National Facility for Data Handling and Analysis, Human TechnopoleComputational Biology Research Center, Human TechnopoleCell and Plant Physiology Laboratory, CNRS, CEA, INRAE, IRIG, Université Grenoble AlpesUniversity Grenoble Alpes, CEA, IRIG-MEMInstitute of Computer Science, University of GöttingenInstitute of Computer Science, University of GöttingenCell and Plant Physiology Laboratory, CNRS, CEA, INRAE, IRIG, Université Grenoble AlpesComputational Biology Research Center, Human TechnopoleBioimage Analysis Unit, National Facility for Data Handling and Analysis, Human TechnopoleAbstract Analysis of biological images relies heavily on segmenting the biological objects of interest in the image before performing quantitative analysis. Deep learning (DL) is ubiquitous in such segmentation tasks, but can be cumbersome to apply, as it often requires a large amount of manual labeling to produce ground-truth data, and expert knowledge to train the models. More recently, large foundation models, such as SAM, have shown promising results on scientific images. They, however, require manual prompting for each object or tedious post-processing to selectively segment these objects. Here, we present FeatureForest, a method that leverages the feature embeddings of large foundation models to train a random forest classifier, thereby providing users with a rapid way of semantically segmenting complex images using only a few labeling strokes. We demonstrate the improvement in performance over a variety of datasets and provide an open-source implementation in napari that can be extended to new models.https://doi.org/10.1038/s44303-025-00089-9 |
| spellingShingle | Mehdi Seifi Damian Dalle Nogare Juan Manuel Battagliotti Vera Galinova Ananya Kedige Rao Pierre-Henri Jouneau Anwai Archit AI4Life Horizon Europe Programme Consortium Constantin Pape Johan Decelle Florian Jug Joran Deschamps FeatureForest: the power of foundation models, the usability of random forests npj Imaging |
| title | FeatureForest: the power of foundation models, the usability of random forests |
| title_full | FeatureForest: the power of foundation models, the usability of random forests |
| title_fullStr | FeatureForest: the power of foundation models, the usability of random forests |
| title_full_unstemmed | FeatureForest: the power of foundation models, the usability of random forests |
| title_short | FeatureForest: the power of foundation models, the usability of random forests |
| title_sort | featureforest the power of foundation models the usability of random forests |
| url | https://doi.org/10.1038/s44303-025-00089-9 |
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