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Automated analysis of whole slide digital skin biopsy images
A rapidly increasing rate of melanoma diagnosis has been noted over the past three decades, and nearly 1 in 4 skin biopsies are diagnosed as melanocytic lesions. The gold standard for diagnosis of melanoma is the histopathological examination by a pathologist to analyze biopsy material at both the c...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531680/ https://www.ncbi.nlm.nih.gov/pubmed/36204597 http://dx.doi.org/10.3389/frai.2022.1005086 |
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author | Nofallah, Shima Wu, Wenjun Liu, Kechun Ghezloo, Fatemeh Elmore, Joann G. Shapiro, Linda G. |
author_facet | Nofallah, Shima Wu, Wenjun Liu, Kechun Ghezloo, Fatemeh Elmore, Joann G. Shapiro, Linda G. |
author_sort | Nofallah, Shima |
collection | PubMed |
description | A rapidly increasing rate of melanoma diagnosis has been noted over the past three decades, and nearly 1 in 4 skin biopsies are diagnosed as melanocytic lesions. The gold standard for diagnosis of melanoma is the histopathological examination by a pathologist to analyze biopsy material at both the cellular and structural levels. A pathologist's diagnosis is often subjective and prone to variability, while deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. Mitoses are important entities when reviewing skin biopsy cases as their presence carries prognostic information; thus, their precise detection is an important factor for clinical care. In addition, semantic segmentation of clinically important structures in skin biopsies might help the diagnosis pipeline with an accurate classification. We aim to provide prognostic and diagnostic information on skin biopsy images, including the detection of cellular level entities, segmentation of clinically important tissue structures, and other important factors toward the accurate diagnosis of skin biopsy images. This paper is an overview of our work on analysis of digital whole slide skin biopsy images, including mitotic figure (mitosis) detection, semantic segmentation, diagnosis, and analysis of pathologists' viewing patterns, and with new work on melanocyte detection. Deep learning has been applied to our methods for all the detection, segmentation, and diagnosis work. In our studies, deep learning is proven superior to prior approaches to skin biopsy analysis. Our work on analysis of pathologists' viewing patterns is the only such work in the skin biopsy literature. Our work covers the whole spectrum from low-level entities through diagnosis and understanding what pathologists do in performing their diagnoses. |
format | Online Article Text |
id | pubmed-9531680 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95316802022-10-05 Automated analysis of whole slide digital skin biopsy images Nofallah, Shima Wu, Wenjun Liu, Kechun Ghezloo, Fatemeh Elmore, Joann G. Shapiro, Linda G. Front Artif Intell Artificial Intelligence A rapidly increasing rate of melanoma diagnosis has been noted over the past three decades, and nearly 1 in 4 skin biopsies are diagnosed as melanocytic lesions. The gold standard for diagnosis of melanoma is the histopathological examination by a pathologist to analyze biopsy material at both the cellular and structural levels. A pathologist's diagnosis is often subjective and prone to variability, while deep learning image analysis methods may improve and complement current diagnostic and prognostic capabilities. Mitoses are important entities when reviewing skin biopsy cases as their presence carries prognostic information; thus, their precise detection is an important factor for clinical care. In addition, semantic segmentation of clinically important structures in skin biopsies might help the diagnosis pipeline with an accurate classification. We aim to provide prognostic and diagnostic information on skin biopsy images, including the detection of cellular level entities, segmentation of clinically important tissue structures, and other important factors toward the accurate diagnosis of skin biopsy images. This paper is an overview of our work on analysis of digital whole slide skin biopsy images, including mitotic figure (mitosis) detection, semantic segmentation, diagnosis, and analysis of pathologists' viewing patterns, and with new work on melanocyte detection. Deep learning has been applied to our methods for all the detection, segmentation, and diagnosis work. In our studies, deep learning is proven superior to prior approaches to skin biopsy analysis. Our work on analysis of pathologists' viewing patterns is the only such work in the skin biopsy literature. Our work covers the whole spectrum from low-level entities through diagnosis and understanding what pathologists do in performing their diagnoses. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9531680/ /pubmed/36204597 http://dx.doi.org/10.3389/frai.2022.1005086 Text en Copyright © 2022 Nofallah, Wu, Liu, Ghezloo, Elmore and Shapiro. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Nofallah, Shima Wu, Wenjun Liu, Kechun Ghezloo, Fatemeh Elmore, Joann G. Shapiro, Linda G. Automated analysis of whole slide digital skin biopsy images |
title | Automated analysis of whole slide digital skin biopsy images |
title_full | Automated analysis of whole slide digital skin biopsy images |
title_fullStr | Automated analysis of whole slide digital skin biopsy images |
title_full_unstemmed | Automated analysis of whole slide digital skin biopsy images |
title_short | Automated analysis of whole slide digital skin biopsy images |
title_sort | automated analysis of whole slide digital skin biopsy images |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531680/ https://www.ncbi.nlm.nih.gov/pubmed/36204597 http://dx.doi.org/10.3389/frai.2022.1005086 |
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