A hybrid approach for incorporating deep visual features and side channel information with applications to AMD detection

Arnaldo Horta, Neil Joshi, Michael Pekala, Katia D. Pacheco, Jun Kong, Neil Bressler, David E. Freund, Philippe Burlina

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work investigates a hybrid method based on random forests and deep image features to combine non-visual side channel information with image data for classification. We apply this to automated retinal image analysis (ARIA) and the detection of age-related macular degeneration (AMD). For evaluation, we use a dataset collected by the National Institute of Health with over 4000 study participants. The non-visual side channel data includes information related to demographics (e.g. ethnicity), lifestyle (e.g. sunlight exposure), and prior conditions (e.g. cataracts). Our study, which compares the performance of different feature combinations, offers preliminary results that constitute a baseline for future investigations on joint deep visual and side channel feature exploitation for AMD detection. This approach could potentially be used for other medical image analysis problems.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
EditorsXuewen Chen, Bo Luo, Feng Luo, Vasile Palade, M. Arif Wani
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages716-720
Number of pages5
ISBN (Electronic)9781538614174
DOIs
StatePublished - Jan 1 2017
Event16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 - Cancun, Mexico
Duration: Dec 18 2017Dec 21 2017

Publication series

NameProceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Volume2017-December

Other

Other16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
CountryMexico
CityCancun
Period12/18/1712/21/17

Keywords

  • AMD
  • age related macular degeneration
  • deep learning
  • non tenso features
  • side channel incorporation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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  • Cite this

    Horta, A., Joshi, N., Pekala, M., Pacheco, K. D., Kong, J., Bressler, N., Freund, D. E., & Burlina, P. (2017). A hybrid approach for incorporating deep visual features and side channel information with applications to AMD detection. In X. Chen, B. Luo, F. Luo, V. Palade, & M. A. Wani (Eds.), Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017 (pp. 716-720). (Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2017.00-75