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Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images
Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and clas...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867018/ https://www.ncbi.nlm.nih.gov/pubmed/36676197 http://dx.doi.org/10.3390/life13010247 |
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author | Li, Jianqiang Xu, Qinlan Cheng, Wenxiu Zhao, Linna Liu, Suqin Gao, Zhengkai Xu, Xi Ye, Caihua You, Huanling |
author_facet | Li, Jianqiang Xu, Qinlan Cheng, Wenxiu Zhao, Linna Liu, Suqin Gao, Zhengkai Xu, Xi Ye, Caihua You, Huanling |
author_sort | Li, Jianqiang |
collection | PubMed |
description | Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels). |
format | Online Article Text |
id | pubmed-9867018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98670182023-01-22 Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images Li, Jianqiang Xu, Qinlan Cheng, Wenxiu Zhao, Linna Liu, Suqin Gao, Zhengkai Xu, Xi Ye, Caihua You, Huanling Life (Basel) Article Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels). MDPI 2023-01-16 /pmc/articles/PMC9867018/ /pubmed/36676197 http://dx.doi.org/10.3390/life13010247 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Jianqiang Xu, Qinlan Cheng, Wenxiu Zhao, Linna Liu, Suqin Gao, Zhengkai Xu, Xi Ye, Caihua You, Huanling Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images |
title | Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images |
title_full | Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images |
title_fullStr | Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images |
title_full_unstemmed | Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images |
title_short | Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images |
title_sort | weakly supervised collaborative learning for airborne pollen segmentation and classification from sem images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867018/ https://www.ncbi.nlm.nih.gov/pubmed/36676197 http://dx.doi.org/10.3390/life13010247 |
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