Automatic Quality Assessment of Reflectance Confocal Microscopy Mosaics using Attention-Based Deep Neural Network

Marek Wodzinski, Miroslawa Pajak, Andrzej Skalski, Alexander Witkowski, Giovanni Pellacani, Joanna Ludzik

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

1 Scopus citations

Abstract

Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.

Original languageEnglish (US)
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1824-1827
Number of pages4
ISBN (Electronic)9781728119908
DOIs
StatePublished - Jul 2020
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: Jul 20 2020Jul 24 2020

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period7/20/207/24/20

Keywords

  • convolutional neural network
  • deep learning
  • melanoma
  • reflectance confocal microscopy
  • skin lesion

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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