Deep learning image analysis for filamentous fungi taxonomic classification: Dealing with small data sets with class imbalance and hierarchical grouping
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Abstract
Deep learning applications in taxonomic classification for animals and plants from images have become popular, while those for microorganisms are still lagging behind. Our study investigated the potential of deep learning for the taxonomic classification of hundreds of filamentous fungi from colony images, which is typically a task that requires specialized knowledge. We isolated soil fungi, annotated their taxonomy using standard molecular barcode techniques, and took images of the fungal colonies grown in petri dishes (n = 606). We applied a convolutional neural network with multiple training approaches and model architectures to deal with some common issues in ecological datasets: small amounts of data, class imbalance, and hierarchically structured grouping. Model performance was overall low due mainly to the relatively small data set, class imbalance, and the high morphological plasticity exhibited by fungal colonies. However, our approach indicates that morphological features such as color, patchiness, and colony extension rate, could be used for the recognition of fungal colonies at higher taxonomic ranks (i.e., phylum, class, and order). Model explanation implies that image recognition characters appear at different positions within the colony (e.g., outer or inner hyphae), depending on the taxonomic resolution. Our study suggests the potential of deep learning applications for better understanding the taxonomy and ecology of filamentous fungi amenable to axenic culturing. Meanwhile, our study also highlights some technical challenges in deep learning image analysis in ecology, highlighting that the domain of applicability of these methods needs to be carefully considered.
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(Fig. 3a).
This text in figure 3 is small, making it difficult to read.
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Figure 1. Example of a scanned image of cultures in the dataset (upper). Circles denote Petri dish objectdetection using the Circle Hough Transform. A zoom in on the bottom-left sample as an example of a filamentousfungus (lower).5.CC-BY-NC-ND 4.0 International licensemade available under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It isThe copyright holder for this preprintthis version posted June 19, 2023.;https://doi.org/10.1101/2023.06.19.545596doi:bioRxiv preprint
It would be useful to have a scale bar in the image so the reader can assess the size of the objects. It would also be useful to have labels denoting the taxonomy of the samples.
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It's useful to have a deep learning method for taxonomic classification of species. However, for the method to be broadly useful across the scientific community the specific methods need to be accessible. Do you have a Github repository where you share the code for this work so that other scientists can try this method on their own biological samples?
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