Automated Classification Map Generation of Prostate Cancer using Deep Learning

Wenhan Tan, David E. Breen, Fernando U. Garcia, Mark D. Zarella

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Whole slide images are examined by pathologists and scored according to the Gleason grading system. It is a time-consuming task and may involve assessing variability between different pathologists. In this work, a deep learning system is presented that generates classification maps for whole slide images. This system produces patch-level results first and then predicts a classification map for each prostate cancer slide. The classification maps contain regional cancer severity for each biopsy and are compared with provided mask images. Both provided mask images and predicted mask images are then reviewed by an experienced pathologist to evaluate classification performance. Most state-of-the-art deep learning methods cannot explain how they output classification results. With this work's classification maps, pathologists can see the regional classification results that explain the algorithm's classification.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
EditorsYufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2064-2071
Number of pages8
ISBN (Electronic)9781665401265
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States
Duration: Dec 9 2021Dec 12 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/9/2112/12/21

Keywords

  • Gleason grading system
  • classification maps
  • convolutional neural network
  • deep learning
  • prostate cancer
  • whole slide image

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Health Informatics
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Automated Classification Map Generation of Prostate Cancer using Deep Learning'. Together they form a unique fingerprint.

Cite this