Towards automated melanoma detection with deep learning: Data purification and augmentation

Devansh Bisla, Anna Choromanska, Russell S. Berman, Jennifer A. Stein, David Polsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Melanoma is one of ten most common cancers in the US. Early detection is crucial for survival, but often the cancer is diagnosed in the fatal stage. Deep learning has the potential to improve cancer detection rates, but its applicability to melanoma detection is compromised by the limitations of the available skin lesion data bases, which are small, heavily imbalanced, and contain images with occlusions. We build deep-learning-based tools for data purification and augmentation to counter-act these limitations. The developed tools can be utilized in a deep learning system for lesion classification and we show how to build such system. The system heavily relies on the processing unit for removing image occlusions and the data generation unit, based on generative adversarial networks, for populating scarce lesion classes, or equivalently creating virtual patients with pre-defined types of lesions. We empirically verify our approach and show that incorporating these two units into melanoma detection system results in the superior performance over common baselines.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages2720-2728
Number of pages9
ISBN (Electronic)9781728125060
DOIs
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: Jun 16 2019Jun 20 2019

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
CountryUnited States
CityLong Beach
Period6/16/196/20/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Bisla, D., Choromanska, A., Berman, R. S., Stein, J. A., & Polsky, D. (2019). Towards automated melanoma detection with deep learning: Data purification and augmentation. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 2720-2728). [9025648] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2019.00330