A high-throughput tumor location system with deep learning for colorectal cancer histopathology image

Jing Ke, Yiqing Shen, Yi Guo, Jason D. Wright, Naifeng Jing, Xiaoyao Liang

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

4 Citations (Scopus)

Abstract

![CDATA[Colorectal cancer is one of the major causes of morbidity and mortality worldwide, however, when discovered at an early stage, it is highly treatable. As the number of specimens increases every year, there has been a boost in the diagnostic workload on pathologists in recent years. In parallel to the development of digital pathology, deep learning has demonstrated its strong capability in feature extraction and interpretation in a variety of medical applications. In this paper, we propose a high-throughput whole-slide image (WSI) analysis system to localize tumor regions accurately with a patch-based convolutional neural network (CNN). We employ Monte Carlo adaptive sampling for a fast detection of tumors at slide level and a conditional random field (CRF) model to integrate spatial correlation for better classification accuracy. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) for performance evaluation. Compared with the regular WSI analysis, the experimental benchmark shows an obvious decrease in processing time while a noticeable improvement in classification accuracy.]]
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25-28, 2020
PublisherSpringer Nature
Pages260-269
Number of pages10
ISBN (Print)9783030591366
DOIs
Publication statusPublished - 2020
EventInternational Conference on Artificial Intelligence in Medicine -
Duration: 25 Aug 2020 → …

Publication series

Name
ISSN (Print)0302-9743

Conference

ConferenceInternational Conference on Artificial Intelligence in Medicine
Period25/08/20 → …

Keywords

  • cancer
  • colon (anatomy)
  • high-throughput diagnosis
  • histopathology
  • rectum

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