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A high-resolution large-scale dataset of pathological and normal white blood cells
Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356748/ https://www.ncbi.nlm.nih.gov/pubmed/37468490 http://dx.doi.org/10.1038/s41597-023-02378-7 |
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author | Bodzas, Alexandra Kodytek, Pavel Zidek, Jan |
author_facet | Bodzas, Alexandra Kodytek, Pavel Zidek, Jan |
author_sort | Bodzas, Alexandra |
collection | PubMed |
description | Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not only time consuming, but can lead to human errors. Although automated and semi-automated systems have been developed in recent years, their high purchasing and maintenance costs make them unaffordable for many medical institutions. Even though much research has been conducted lately to explore more accurate and feasible solutions, most researchers had to deal with a lack of medical data. To address the lack of large-scale databases in this field, we created a high-resolution dataset containing a total of 16027 annotated white blood cells. Moreover, the dataset covers overall 9 types of white blood cells, including clinically significant pathological findings. Since we used high-quality acquisition equipment, the dataset provides one of the highest quality images of blood cells, achieving an approximate resolution of 42 pixels per 1 μm. |
format | Online Article Text |
id | pubmed-10356748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103567482023-07-21 A high-resolution large-scale dataset of pathological and normal white blood cells Bodzas, Alexandra Kodytek, Pavel Zidek, Jan Sci Data Data Descriptor Microscopic examination plays a significant role in the initial screening for a variety of hematological, as well as non-hematological, diagnoses. Microscopic blood smear examination that is considered a key diagnostic technique, is in recent clinical practice still performed manually, which is not only time consuming, but can lead to human errors. Although automated and semi-automated systems have been developed in recent years, their high purchasing and maintenance costs make them unaffordable for many medical institutions. Even though much research has been conducted lately to explore more accurate and feasible solutions, most researchers had to deal with a lack of medical data. To address the lack of large-scale databases in this field, we created a high-resolution dataset containing a total of 16027 annotated white blood cells. Moreover, the dataset covers overall 9 types of white blood cells, including clinically significant pathological findings. Since we used high-quality acquisition equipment, the dataset provides one of the highest quality images of blood cells, achieving an approximate resolution of 42 pixels per 1 μm. Nature Publishing Group UK 2023-07-19 /pmc/articles/PMC10356748/ /pubmed/37468490 http://dx.doi.org/10.1038/s41597-023-02378-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Bodzas, Alexandra Kodytek, Pavel Zidek, Jan A high-resolution large-scale dataset of pathological and normal white blood cells |
title | A high-resolution large-scale dataset of pathological and normal white blood cells |
title_full | A high-resolution large-scale dataset of pathological and normal white blood cells |
title_fullStr | A high-resolution large-scale dataset of pathological and normal white blood cells |
title_full_unstemmed | A high-resolution large-scale dataset of pathological and normal white blood cells |
title_short | A high-resolution large-scale dataset of pathological and normal white blood cells |
title_sort | high-resolution large-scale dataset of pathological and normal white blood cells |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356748/ https://www.ncbi.nlm.nih.gov/pubmed/37468490 http://dx.doi.org/10.1038/s41597-023-02378-7 |
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