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Accelerating Cancer Research With Deep Learning

A representation of a deep learning neural network designed to intelligently extract text-based information from cancer pathology reports.

Researchers at Oak Ridge National Laboratory are using deep-learning techniques to automate information collection from cancer pathology reports.

Credit: Oak Ridge National Laboratory

Oak Ridge National Laboratory (ORNL) researchers are applying deep-learning techniques to automate how information is collected from cancer pathology reports documented across a nationwide network of cancer registry programs.

Georgia Tourassi, director of the Health Data Sciences Institute at ORNL, led a team focused on software that can identify valuable information in cancer reports faster than manual methods.

The machine-learning technique leverages algorithms, big data, and the processing power of the Titan supercomputer at the Oak Ridge Leadership Computing Facility. Using a dataset of nearly 2,000 pathology reports, researchers trained a deep-learning algorithm to simultaneously carry out two closely related tasks.

First the algorithm scanned each report to identify the location of the cancer, and then identified the side of the body on which the cancer was located. Another study used more than 900 reports on breast and lung cancer to test the system's ability to match the cancer's origin to its corresponding classification, using a convolutional neural network and text from general, medical, and highly specialized sources. The algorithm created a mathematical model that drew connections between words shared between unrelated texts.

The researchers say the continued development of automated data tools will give scientists and policymakers a highly detailed view of the U.S. cancer population.

From Oak Ridge National Laboratory
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Abstracts Copyright © 2016 Information Inc., Bethesda, Maryland, USA


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