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Lesion identification and malignancy prediction from clinical dermatological images

We consider machine-learning-based lesion identification and malignancy prediction from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal o...

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Autores principales: Xia, Meng, Kheterpal, Meenal K., Wong, Samantha C., Park, Christine, Ratliff, William, Carin, Lawrence, Henao, Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508136/
https://www.ncbi.nlm.nih.gov/pubmed/36151257
http://dx.doi.org/10.1038/s41598-022-20168-w
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author Xia, Meng
Kheterpal, Meenal K.
Wong, Samantha C.
Park, Christine
Ratliff, William
Carin, Lawrence
Henao, Ricardo
author_facet Xia, Meng
Kheterpal, Meenal K.
Wong, Samantha C.
Park, Christine
Ratliff, William
Carin, Lawrence
Henao, Ricardo
author_sort Xia, Meng
collection PubMed
description We consider machine-learning-based lesion identification and malignancy prediction from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy that can be used for high-level screening processes. Further, we consider augmenting the proposed approach with clinical covariates (from electronic health records) and publicly available data (the ISIC dataset). Comprehensive experiments validated on an independent test dataset demonstrate that (1) the proposed approach outperforms alternative model architectures; (2) the model based on images outperforms a pure clinical model by a large margin, and the combination of images and clinical data does not significantly improves over the image-only model; and (3) the proposed framework offers comparable performance in terms of malignancy classification relative to three board certified dermatologists with different levels of experience.
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spelling pubmed-95081362022-09-25 Lesion identification and malignancy prediction from clinical dermatological images Xia, Meng Kheterpal, Meenal K. Wong, Samantha C. Park, Christine Ratliff, William Carin, Lawrence Henao, Ricardo Sci Rep Article We consider machine-learning-based lesion identification and malignancy prediction from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy that can be used for high-level screening processes. Further, we consider augmenting the proposed approach with clinical covariates (from electronic health records) and publicly available data (the ISIC dataset). Comprehensive experiments validated on an independent test dataset demonstrate that (1) the proposed approach outperforms alternative model architectures; (2) the model based on images outperforms a pure clinical model by a large margin, and the combination of images and clinical data does not significantly improves over the image-only model; and (3) the proposed framework offers comparable performance in terms of malignancy classification relative to three board certified dermatologists with different levels of experience. Nature Publishing Group UK 2022-09-23 /pmc/articles/PMC9508136/ /pubmed/36151257 http://dx.doi.org/10.1038/s41598-022-20168-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Xia, Meng
Kheterpal, Meenal K.
Wong, Samantha C.
Park, Christine
Ratliff, William
Carin, Lawrence
Henao, Ricardo
Lesion identification and malignancy prediction from clinical dermatological images
title Lesion identification and malignancy prediction from clinical dermatological images
title_full Lesion identification and malignancy prediction from clinical dermatological images
title_fullStr Lesion identification and malignancy prediction from clinical dermatological images
title_full_unstemmed Lesion identification and malignancy prediction from clinical dermatological images
title_short Lesion identification and malignancy prediction from clinical dermatological images
title_sort lesion identification and malignancy prediction from clinical dermatological images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9508136/
https://www.ncbi.nlm.nih.gov/pubmed/36151257
http://dx.doi.org/10.1038/s41598-022-20168-w
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