Automated high-grade prostate cancer detection and ranking on whole slide images

Chao Hui Huang, Daniel Racoceanu

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

4 Scopus citations

Abstract

Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection (∼95:57% ± 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationDigital Pathology
EditorsMetin N. Gurcan, John E. Tomaszewski
PublisherSPIE
ISBN (Electronic)9781510607255
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Digital Pathology - Orlando, United States
Duration: 12 Feb 201713 Feb 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10140
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2017: Digital Pathology
Country/TerritoryUnited States
CityOrlando
Period12/02/1713/02/17

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