Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

Saad Ullah Akram*, Talha Qaiser, Simon Graham, Juho Kannala, Janne Heikkilä, Nasir Rajpoot

*Corresponding author for this work

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

13 Citations (Scopus)

Abstract

Mitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task is very time-consuming, tedious and subjective. Automated mitosis detection methods have made great advances in recent years. However, these methods require exhaustive labeling of a large number of selected regions of interest. This task is very expensive because expert pathologists are needed for reliable and accurate annotations. In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs. As a result, our method capitalizes on the growing number of digitized histology images, without relying on exhaustive annotations, subsequently improving mitosis detection. Our method first learns a mitosis detector from labeled data, uses this detector to mine additional mitosis samples from unlabeled WSIs, and then trains the final model using this larger and diverse set of mitosis samples. The use of unlabeled data improves F1-score by ∼ 5% compared to our best performing fully-supervised model on the TUPAC validation set. Our submission (single model) to TUPAC challenge ranks highly on the leaderboard with an F1-score of 0.64.

Original languageEnglish
Title of host publicationComputational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
Pages69-77
Number of pages9
ISBN (Electronic)9783030009496
DOIs
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Computational Pathology - Granada, Spain
Duration: 16 Sep 201820 Sep 2018
Conference number: 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume11039 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

WorkshopInternational Workshop on Computational Pathology
Abbreviated titleCOMPAY
Country/TerritorySpain
CityGranada
Period16/09/201820/09/2018

Keywords

  • Breast cancer
  • Computational pathology
  • Mitosis detection
  • Self-supervised learning
  • Semi-supervised learning

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