Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images.
Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the...
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ftdoajarticles:oai:doaj.org/article:160f87df41124ccc9bea66ac757b98d1 2023-05-15T16:28:06+02:00 Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images. Iver Martinsen Alf Harbitz Filippo Maria Bianchi 2022-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0277244 https://doaj.org/article/160f87df41124ccc9bea66ac757b98d1 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pone.0277244 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0277244 https://doaj.org/article/160f87df41124ccc9bea66ac757b98d1 PLoS ONE, Vol 17, Iss 11, p e0277244 (2022) Medicine R Science Q article 2022 ftdoajarticles https://doi.org/10.1371/journal.pone.0277244 2022-12-30T19:38:59Z Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the same human expert might result in different ages assigned to the same otolith, in addition to an inherent bias between readers. To improve efficiency and resolve inconsistent results in the age reading from otolith images by human experts, an automated procedure based on convolutional neural networks (CNNs), a class of deep learning models suitable for image processing, is investigated. We applied CNNs that perform image regression to estimate the age of Greenland halibut (Reinhardtius hippoglossoides) with good results for individual ages as well as the overall age distribution, with an average CV of about 10% relative to the read ages by experts. In addition, the density distribution of predicted ages resembles the density distribution of the ground truth. By using k*l-fold cross-validation, we test all available samples, and we show that the results are rather sensitive to the choice of test set. Finally, we apply explanation techniques to analyze the decision process of deep learning models. In particular, we produce heatmaps indicating which input features that are the most important in the computation of predicted age. Article in Journal/Newspaper Greenland Directory of Open Access Journals: DOAJ Articles Greenland PLOS ONE 17 11 e0277244 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Medicine R Science Q |
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Medicine R Science Q Iver Martinsen Alf Harbitz Filippo Maria Bianchi Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images. |
topic_facet |
Medicine R Science Q |
description |
Otoliths (ear-stones) in the inner ears of vertebrates containing visible year zones are used extensively to determine fish age. Analysis of otoliths is a time-consuming and difficult task that requires the education of human experts. Human age estimates are inconsistent, as several readings by the same human expert might result in different ages assigned to the same otolith, in addition to an inherent bias between readers. To improve efficiency and resolve inconsistent results in the age reading from otolith images by human experts, an automated procedure based on convolutional neural networks (CNNs), a class of deep learning models suitable for image processing, is investigated. We applied CNNs that perform image regression to estimate the age of Greenland halibut (Reinhardtius hippoglossoides) with good results for individual ages as well as the overall age distribution, with an average CV of about 10% relative to the read ages by experts. In addition, the density distribution of predicted ages resembles the density distribution of the ground truth. By using k*l-fold cross-validation, we test all available samples, and we show that the results are rather sensitive to the choice of test set. Finally, we apply explanation techniques to analyze the decision process of deep learning models. In particular, we produce heatmaps indicating which input features that are the most important in the computation of predicted age. |
format |
Article in Journal/Newspaper |
author |
Iver Martinsen Alf Harbitz Filippo Maria Bianchi |
author_facet |
Iver Martinsen Alf Harbitz Filippo Maria Bianchi |
author_sort |
Iver Martinsen |
title |
Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images. |
title_short |
Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images. |
title_full |
Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images. |
title_fullStr |
Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images. |
title_full_unstemmed |
Age prediction by deep learning applied to Greenland halibut (Reinhardtius hippoglossoides) otolith images. |
title_sort |
age prediction by deep learning applied to greenland halibut (reinhardtius hippoglossoides) otolith images. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2022 |
url |
https://doi.org/10.1371/journal.pone.0277244 https://doaj.org/article/160f87df41124ccc9bea66ac757b98d1 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
PLoS ONE, Vol 17, Iss 11, p e0277244 (2022) |
op_relation |
https://doi.org/10.1371/journal.pone.0277244 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0277244 https://doaj.org/article/160f87df41124ccc9bea66ac757b98d1 |
op_doi |
https://doi.org/10.1371/journal.pone.0277244 |
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PLOS ONE |
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17 |
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11 |
container_start_page |
e0277244 |
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