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Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images

SIMPLE SUMMARY: Pollen allergy is a highly prevalent disease affecting humans worldwide. Early pollen identification can help allergic individuals to prevent pollinosis. Recently, automatic pollen identification (API) has been shown to play a prominent role in pollen concentration monitoring. Develo...

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Detalles Bibliográficos
Autores principales: Zhao, Lin-Na, Li, Jian-Qiang, Cheng, Wen-Xiu, Liu, Su-Qin, Gao, Zheng-Kai, Xu, Xi, Ye, Cai-Hua, You, Huan-Ling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775008/
https://www.ncbi.nlm.nih.gov/pubmed/36552349
http://dx.doi.org/10.3390/biology11121841
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author Zhao, Lin-Na
Li, Jian-Qiang
Cheng, Wen-Xiu
Liu, Su-Qin
Gao, Zheng-Kai
Xu, Xi
Ye, Cai-Hua
You, Huan-Ling
author_facet Zhao, Lin-Na
Li, Jian-Qiang
Cheng, Wen-Xiu
Liu, Su-Qin
Gao, Zheng-Kai
Xu, Xi
Ye, Cai-Hua
You, Huan-Ling
author_sort Zhao, Lin-Na
collection PubMed
description SIMPLE SUMMARY: Pollen allergy is a highly prevalent disease affecting humans worldwide. Early pollen identification can help allergic individuals to prevent pollinosis. Recently, automatic pollen identification (API) has been shown to play a prominent role in pollen concentration monitoring. Developing an accurate and effective identification system may provide new insights for pollinosis prevention. This paper presents a novel automatic pollen identification method integrating localization tasks and classification tasks, thus perfectly mimicking the observation process from palynologists. The inter-task dependence and intra-task reliability are simultaneously considered in this method to effectively enhance the pollen identification performance. We believe that our study will contribute to enhancing symptom control of pollen allergy and maintaining the life quality of allergic patients. ABSTRACT: Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at “pollen localization problem”) and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at “pollen classification problem”). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.
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spelling pubmed-97750082022-12-23 Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images Zhao, Lin-Na Li, Jian-Qiang Cheng, Wen-Xiu Liu, Su-Qin Gao, Zheng-Kai Xu, Xi Ye, Cai-Hua You, Huan-Ling Biology (Basel) Article SIMPLE SUMMARY: Pollen allergy is a highly prevalent disease affecting humans worldwide. Early pollen identification can help allergic individuals to prevent pollinosis. Recently, automatic pollen identification (API) has been shown to play a prominent role in pollen concentration monitoring. Developing an accurate and effective identification system may provide new insights for pollinosis prevention. This paper presents a novel automatic pollen identification method integrating localization tasks and classification tasks, thus perfectly mimicking the observation process from palynologists. The inter-task dependence and intra-task reliability are simultaneously considered in this method to effectively enhance the pollen identification performance. We believe that our study will contribute to enhancing symptom control of pollen allergy and maintaining the life quality of allergic patients. ABSTRACT: Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at “pollen localization problem”) and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at “pollen classification problem”). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method. MDPI 2022-12-16 /pmc/articles/PMC9775008/ /pubmed/36552349 http://dx.doi.org/10.3390/biology11121841 Text en © 2022 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
Zhao, Lin-Na
Li, Jian-Qiang
Cheng, Wen-Xiu
Liu, Su-Qin
Gao, Zheng-Kai
Xu, Xi
Ye, Cai-Hua
You, Huan-Ling
Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images
title Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images
title_full Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images
title_fullStr Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images
title_full_unstemmed Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images
title_short Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images
title_sort simulation palynologists for pollinosis prevention: a progressive learning of pollen localization and classification for whole slide images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775008/
https://www.ncbi.nlm.nih.gov/pubmed/36552349
http://dx.doi.org/10.3390/biology11121841
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