Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • University of Oulu
  • University of Warwick
  • Alan Turing Institute

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoComputational Pathology and Ophthalmic Medical Image Analysis - First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
TilaJulkaistu - 1 tammikuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Workshop on Computational Pathology - Granada, Espanja
Kesto: 16 syyskuuta 201820 syyskuuta 2018
Konferenssinumero: 1

Julkaisusarja

NimiLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
KustantajaSpringer
Vuosikerta11039 LNCS
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349

Workshop

WorkshopInternational Workshop on Computational Pathology
LyhennettäCOMPAY
MaaEspanja
KaupunkiGranada
Ajanjakso16/09/201820/09/2018

ID: 28606551