Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas

B. Oh; Y. S. Chu; S. Lee; S. G. Lee; K. Y. Chung; M. R. Roh; K. D. Seo; S. Yang

doi:10.1117/12.2648034

Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas

B. Oh, Y. S. Chu, S. Lee, S. G. Lee, K. Y. Chung, M. R. Roh, K. D. Seo, S. Yang

Department of Dermatology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In Asians, melanoma appears as pigmented lesions on the hands and feet, and is often diagnosed as acral malignant melanoma (ALM) in the late stage with a very poor prognosis. Among diverse clinical characteristics of melanoma, the presence of basement membrane involvement is one of the most important prognostic factors. However, there have been few studies reporting artificial intelligence for prediction of basement membrane involvement in ALMs beyond its diagnosis. Therefore, in this study, we present a deep learning model that predicts the basement membrane involvement of ALMs from dermoscopy images.

Original language	English
Title of host publication	Photonics in Dermatology and Plastic Surgery 2023
Editors	Bernard Choi, Haishan Zeng
Publisher	SPIE
ISBN (Electronic)	9781510658097
DOIs	https://doi.org/10.1117/12.2648034
Publication status	Published - 2023
Event	Photonics in Dermatology and Plastic Surgery 2023 - San Francisco, United States Duration: 2023 Jan 28 → 2023 Jan 29

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	12352
ISSN (Print)	1605-7422

Conference

Conference	Photonics in Dermatology and Plastic Surgery 2023
Country/Territory	United States
City	San Francisco
Period	23/1/28 → 23/1/29

Bibliographical note

Publisher Copyright:
© 2023 SPIE.

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Biomaterials
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1117/12.2648034

Cite this

Oh, B., Chu, Y. S., Lee, S., Lee, S. G., Chung, K. Y., Roh, M. R., Seo, K. D., & Yang, S. (2023). Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas. In B. Choi, & H. Zeng (Eds.), Photonics in Dermatology and Plastic Surgery 2023 Article 123520D (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12352). SPIE. https://doi.org/10.1117/12.2648034

@inproceedings{69e1ed7fe9594a46955f74f29c6af402,

title = "Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas",

abstract = "In Asians, melanoma appears as pigmented lesions on the hands and feet, and is often diagnosed as acral malignant melanoma (ALM) in the late stage with a very poor prognosis. Among diverse clinical characteristics of melanoma, the presence of basement membrane involvement is one of the most important prognostic factors. However, there have been few studies reporting artificial intelligence for prediction of basement membrane involvement in ALMs beyond its diagnosis. Therefore, in this study, we present a deep learning model that predicts the basement membrane involvement of ALMs from dermoscopy images.",

keywords = "Artificial intelligence, Breslow thickness, Deep learning, Melanoma, Skin cancer, Tumor depth",

author = "B. Oh and Chu, {Y. S.} and S. Lee and Lee, {S. G.} and Chung, {K. Y.} and Roh, {M. R.} and Seo, {K. D.} and S. Yang",

note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Photonics in Dermatology and Plastic Surgery 2023 ; Conference date: 28-01-2023 Through 29-01-2023",

year = "2023",

doi = "10.1117/12.2648034",

language = "English",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Bernard Choi and Haishan Zeng",

booktitle = "Photonics in Dermatology and Plastic Surgery 2023",

address = "United States",

}

Oh, B, Chu, YS, Lee, S, Lee, SG, Chung, KY, Roh, MR, Seo, KD & Yang, S 2023, Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas. in B Choi & H Zeng (eds), Photonics in Dermatology and Plastic Surgery 2023., 123520D, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12352, SPIE, Photonics in Dermatology and Plastic Surgery 2023, San Francisco, United States, 23/1/28. https://doi.org/10.1117/12.2648034

Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas. / Oh, B.; Chu, Y. S.; Lee, S. et al.
Photonics in Dermatology and Plastic Surgery 2023. ed. / Bernard Choi; Haishan Zeng. SPIE, 2023. 123520D (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12352).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas

AU - Oh, B.

AU - Chu, Y. S.

AU - Lee, S.

AU - Lee, S. G.

AU - Chung, K. Y.

AU - Roh, M. R.

AU - Seo, K. D.

AU - Yang, S.

PY - 2023

Y1 - 2023

N2 - In Asians, melanoma appears as pigmented lesions on the hands and feet, and is often diagnosed as acral malignant melanoma (ALM) in the late stage with a very poor prognosis. Among diverse clinical characteristics of melanoma, the presence of basement membrane involvement is one of the most important prognostic factors. However, there have been few studies reporting artificial intelligence for prediction of basement membrane involvement in ALMs beyond its diagnosis. Therefore, in this study, we present a deep learning model that predicts the basement membrane involvement of ALMs from dermoscopy images.

AB - In Asians, melanoma appears as pigmented lesions on the hands and feet, and is often diagnosed as acral malignant melanoma (ALM) in the late stage with a very poor prognosis. Among diverse clinical characteristics of melanoma, the presence of basement membrane involvement is one of the most important prognostic factors. However, there have been few studies reporting artificial intelligence for prediction of basement membrane involvement in ALMs beyond its diagnosis. Therefore, in this study, we present a deep learning model that predicts the basement membrane involvement of ALMs from dermoscopy images.

KW - Artificial intelligence

KW - Breslow thickness

KW - Deep learning

KW - Melanoma

KW - Skin cancer

KW - Tumor depth

UR - http://www.scopus.com/inward/record.url?scp=85159725880&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85159725880&partnerID=8YFLogxK

U2 - 10.1117/12.2648034

DO - 10.1117/12.2648034

M3 - Conference contribution

AN - SCOPUS:85159725880

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Photonics in Dermatology and Plastic Surgery 2023

A2 - Choi, Bernard

A2 - Zeng, Haishan

PB - SPIE

T2 - Photonics in Dermatology and Plastic Surgery 2023

Y2 - 28 January 2023 through 29 January 2023

ER -

Deep Learning Algorithms for Predicting Basement Membrane Involvement of Acral Lentiginous Melanomas

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this