Lesion identification and malignancy prediction from clinical dermatological images.

Journal Article (Journal 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.

Full Text

Duke Authors

Cited Authors

  • Xia, M; Kheterpal, MK; Wong, SC; Park, C; Ratliff, W; Carin, L; Henao, R

Published Date

  • September 23, 2022

Published In

Volume / Issue

  • 12 / 1

Start / End Page

  • 15836 -

PubMed ID

  • 36151257

Pubmed Central ID

  • PMC9508136

Electronic International Standard Serial Number (EISSN)

  • 2045-2322

Digital Object Identifier (DOI)

  • 10.1038/s41598-022-20168-w


  • eng

Conference Location

  • England