Segmentation and Classification of Melanoma and Nevus in Whole Slide Images

Mike Van Zon, Nikolas Stathonikos, Willeke A.M. Blokx, Selim Komina, Sybren L.N. Maas, Josien P.W. Pluim, Paul J. Van Diest, Mitko Veta

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

Abstract

The incidence of skin cancer cases and specifically melanoma has tripled since the 1990s in The Netherlands. The early detection of melanoma can lead to an almost 100% 5-year survival prognosis dropping drastically when detected later. Studies show that pathologists can have a discordance reporting of melanoma to nevi up to 14.3%. An automated method could help support pathologists in diagnosing melanoma and prioritize cases based on a risk assessment. Our method used 563 whole slide images to train and test a system comprising of two models that segment and classify skin sections to melanoma, nevus or negative for both. We used 232 slides for training and validation and the remaining 331 for testing. The first model uses a U-Net architecture to perform a semantic segmentation and the output of that model was used to feed a convolution neural network to classify the WSI with a global label. Our method achieved a Dice score of 0.835\pm 0.08 on the segmentation of the validation set and a weighted F1-score of 0.954 on the independent test dataset. Out of the 176 melanoma slides, the algorithm managed to classify 173 correctly. Out of the 62 nevi slides the algorithm managed to correctly classify 57.

Original languageEnglish
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages263-266
Number of pages4
ISBN (Electronic)9781538693308
DOIs
Publication statusPublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: 3 Apr 20207 Apr 2020

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
CountryUnited States
CityIowa City
Period3/04/207/04/20

Keywords

  • deep learning
  • Digital pathology
  • histopathology
  • melanoma

Fingerprint Dive into the research topics of 'Segmentation and Classification of Melanoma and Nevus in Whole Slide Images'. Together they form a unique fingerprint.

Cite this