Don’t Tear Your Hair Out: Analysis of the Impact of Skin Hair on the Diagnosis of Microscopic Skin Lesions

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Recent work on the classification of microscopic skin lesions does not consider how the presence of skin hair may affect diagnosis. In this work, we investigate how deep-learning models can handle a varying amount of skin hair during their predictions. We present an automated processing pipeline that tests the performance of the classification model. We conclude that, under realistic conditions, modern day classification models are robust to the presence of skin hair and we investigate three architectural choices (Resnet50, InceptionV3, Densenet121) that make them so.

Original languageEnglish
Title of host publicationPattern Recognition. ICPR International Workshops and Challenges, 2021, Proceedings
EditorsAlberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani
PublisherSpringer
Pages406-416
Number of pages11
ISBN (Print)9783030687625
DOIs
Publication statusPublished - 2021
Event25th International Conference on Pattern Recognition Workshops, ICPR 2020 - Milan, Italy
Duration: 10 Jan 202115 Jan 2021
Conference number: 25
https://www.micc.unifi.it/icpr2020/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12661 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Pattern Recognition Workshops, ICPR 2020
Abbreviated titleICPR 2020
Country/TerritoryItaly
CityMilan
Period10/01/2115/01/21
Internet address

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Augmentations
  • Deep learning
  • Dermatology
  • Hair detection
  • Imaging
  • Melanoma
  • Skin lesion

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