Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods

被引：9

作者：

Grossarth, Sarah ^{[1
]}

Mosley, Dominique ^{[2
]}

Madden, Christopher ^{[3
,4
]}

Ike, Jacqueline ^{[3
,5
]}

Smith, Isabelle ^{[3
,6
]}

Huo, Yuankai ^{[7
]}

Wheless, Lee ^{[3
,8
,9
]}

机构：

[1] East Tennessee State Univ, Quillen Coll Med, Johnson City, TN USA

[2] Vanderbilt Univ, Sch Med, Nashville, TN USA

[3] Vanderbilt Univ, Dept Dermatol, Med Ctr, Nashville, TN 37232 USA

[4] SUNY Downstate Coll Med, Brooklyn, NY USA

[5] Meharry Med Coll, Nashville, TN USA

[6] Vanderbilt Univ, Nashville, TN USA

[7] Vanderbilt Univ, Dept Comp Sci & Elect Engn, Nashville, TN 37235 USA

[8] Vanderbilt Univ, Dept Med, Div Epidemiol, Med Ctr, Nashville, TN 37232 USA

[9] Tennessee Valley Healthcare Syst VA Med Ctr, Nashville, TN 37232 USA

来源：

CURRENT ONCOLOGY REPORTS | 2023年 / 25卷 / 06期

关键词：

Artificial intelligence; Melanoma; Machine Learning; Deep learning; Digital pathology; Dermoscopy; CONVOLUTIONAL NEURAL-NETWORK; SKIN-CANCER; ARTIFICIAL-INTELLIGENCE; LEVEL CLASSIFICATION; IMAGE CLASSIFICATION; DERMATOLOGISTS; LESIONS; PERFORMANCE; ALGORITHMS; DERMOSCOPY;

D O I：

10.1007/s11912-023-01407-3

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Purpose of ReviewThe purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma.Recent FindingsDeep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing.There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.

引用

页码：635 / 645

页数：11

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