Multi-task deep neural networks for automated extraction of primary site and laterality information from cancer pathology reports

Hong Jun Yoon, Arvind Ramanathan, Georgia Tourassi

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

18 Scopus citations

Abstract

Automated annotation of free-text cancer pathology reports is a critical challenge for cancer registries and the national cancer surveillance program. In this paper, we investigated deep neural networks (DNNs) for automated extraction of the primary cancer site and its laterality, two fundamental targets of cancer reporting. Our experiments showed that single-task DNNs are capable of extracting information with higher precision and recall than traditional classification methods for the more challenging target. Furthermore, a multi-task learning DNN resulted in further performance improvement. This preliminary study, indicate the strong potential for multi-task deep neural networks to extract cancer-relevant information from free-text pathology reports.

Original languageEnglish
Title of host publicationAdvances in Big Data - Proceedings of the 2nd INNS Conference on Big Data, 2016
EditorsAsim Roy, Marley Vellasco, Yannis Manolopoulos, Lazaros Iliadis, Plamen Angelov
PublisherSpringer Verlag
Pages195-204
Number of pages10
ISBN (Print)9783319478975
DOIs
StatePublished - 2017
Event2nd International Neural Network Society Conference on Big Data, INNS 2016 - Thessaloniki, Greece
Duration: Oct 23 2016Oct 25 2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume529
ISSN (Print)2194-5357

Conference

Conference2nd International Neural Network Society Conference on Big Data, INNS 2016
Country/TerritoryGreece
CityThessaloniki
Period10/23/1610/25/16

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). The study was supported by the Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, under LDRD projects No. 7417 and No. 8231.

FundersFunder number
U.S. Department of Energy
Laboratory Directed Research and Development8231, 7417

    Keywords

    • Cancer pathology report
    • Deep neural network
    • Multi-task learning
    • Natural language processing

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