Selective Information Extraction Strategies for Cancer Pathology Reports with Convolutional Neural Networks

  • Hong-Jun YoonEmail author
  • John X. Qiu
  • J. Blair Christian
  • Jacob Hinkle
  • Folami Alamudun
  • Georgia Tourassi
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)


To trust model predictions, it is important to ensure new data scored by the model comes from the same population used for model training. If the model is used to score new data different than the model’s training data, then predictions and model performance metrics cannot be trusted. Identifying and excluding these anomalous data points is an important task when using models in the real world. Traditional machine learning algorithms and classifiers don’t have the capability to abstain in this case. Here we propose a data-novelty detection algorithm for the Convolutional Neural Network classifier, yielding a rejection score for each new data point scored. It is a post-modeling procedure which examines the distribution of convolution filters to determine if the prediction should be trusted. We apply this algorithm to an information extraction model for a natural language text corpus. We evaluated the algorithm performance using a primary cancer site classification model applied to cancer pathology reports. Results demonstrate that the algorithm is an effective way to exclude cancer pathology reports from model scoring when they do not contain the expected information necessary to accurately classify the primary cancer type.


Novelty detection Uncertainty determination Cancer pathology reports Information extraction Natural language processing Convolutional neural networks 



This work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health. This work was performed under the auspices of the U.S. Department of Energy by Argonne National Laboratory under Contract DE-AC02-06-CH11357, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Los Alamos National Laboratory under Contract DE-AC5206NA25396, and Oak Ridge National Laboratory under Contract DE-AC05-00OR22725.

The authors wish to thank Valentina Petkov of the Surveillance Research Program from the National Cancer Institute and the SEER registry at Connecticut, Hawaii, Kentucky, New Mexico and Seattle for the pathology reports used in this investigation.

This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S., Department of Energy under Contract No. DE-AC05-00OR22725.


  1. 1.
    Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: a system for large-scale machine learning. OSDI 16, 265–283 (2016)Google Scholar
  2. 2.
    Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., Mané, D.: Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 (2016)
  3. 3.
    Chollet, F., et al.: Keras (2015).
  4. 4.
    Deng, Y., Bao, F., Deng, X., Wang, R., Kong, Y., Dai, Q.: Deep and structured robust information theoretic learning for image analysis. IEEE Trans. Image Process. 25(9), 4209–4221 (2016)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Gelman, A., Stern, H.S., Carlin, J.B., Dunson, D.B., Vehtari, A., Rubin, D.B.: Bayesian Data Analysis. Chapman and Hall/CRC, Berkeley (2013)zbMATHGoogle Scholar
  6. 6.
    Goodman, L.A.: On the exact variance of products. J. Am. Stat. Assoc. 55(292), 708–713 (1960)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Goroshin, R., Mathieu, M.F., LeCun, Y.: Learning to linearize under uncertainty. In: Advances in Neural Information Processing Systems, pp. 1234–1242 (2015)Google Scholar
  8. 8.
    Kavuluru, R., Hands, I., Durbin, E.B., Witt, L.: Automatic extraction of ICD-O-3 primary sites from cancer pathology reports. In: AMIA Summits on Translational Science Proceedings 2013, p. 112 (2013)Google Scholar
  9. 9.
    Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  11. 11.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  12. 12.
    Louis, D.N., Ohgaki, H., Wiestler, O.D., Cavenee, W.K., Burger, P.C., Jouvet, A., Scheithauer, B.W., Kleihues, P.: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114(2), 97–109 (2007)CrossRefGoogle Scholar
  13. 13.
    Meystre, S.M., Savova, G.K., Kipper-Schuler, K.C., Hurdle, J.F.: Extracting information from textual documents in the electronic health record: a review of recent research. Yearb. Med. Inform. 17(01), 128–144 (2008)CrossRefGoogle Scholar
  14. 14.
    Nguyen, A., Moore, J., Lawley, M., Hansen, D., Colquist, S.: Automatic extraction of cancer characteristics from free-text pathology reports for cancer notifications. Stud. Health Technol. Inform. 168, 117–124 (2011)Google Scholar
  15. 15.
    Papadopoulos, H.: Inductive conformal prediction: theory and application to neural networks. In: Tools in Artificial Intelligence. InTech (2008)Google Scholar
  16. 16.
    Qiu, J.X., Yoon, H.J., Fearn, P.A., Tourassi, G.D.: Deep learning for automated extraction of primary sites from cancer pathology reports. IEEE J. Biomed. Health Inform. 22(1), 244–251 (2018)CrossRefGoogle Scholar
  17. 17.
    Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9, 371–421 (2008)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Smith, R.C.: Uncertainty Quantification: Theory, Implementation, and Applications, vol. 12. SIAM, Philadelphia (2013)Google Scholar
  19. 19.
    American Cancer Society: Cancer facts & figures. The Society (2018)Google Scholar
  20. 20.
    Yoon, H.J., Robinson, S., Christian, J.B., Qiu, J.X., Tourassi, G.D.: Filter pruning of convolutional neural networks for text classification: a case study of cancer pathology report comprehension. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 345–348. IEEE (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hong-Jun Yoon
    • 1
    Email author
  • John X. Qiu
    • 1
  • J. Blair Christian
    • 1
  • Jacob Hinkle
    • 1
  • Folami Alamudun
    • 1
  • Georgia Tourassi
    • 1
  1. 1.Biomedical Sciences, Engineering and Computing Group, Health Data Sciences InstituteOak Ridge National LaboratoryOak RidgeUSA

Personalised recommendations