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Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports

Publication Type
Conference Paper
Book Title
IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS
Publication Date
Conference Name
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2019)
Conference Location
Chicago, Illinois, United States of America
Conference Sponsor
IEEE
Conference Date
-

Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification m acro F -score o ver the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.