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Cancer Research in NEC Labs America’s Machine Learning Dept.

Cancer Research in NEC Labs America’s Machine Learning Dept. Matt Miller Research Staff Member NEC Labs America. Modern Tools for Oncology. Detailed diagnostics Targeted therapies Methods of mapping diagnostic results to therapies (“personalized medicine”) Methods of testing these tools.

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Cancer Research in NEC Labs America’s Machine Learning Dept.

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  1. Cancer Researchin NEC Labs America’sMachine Learning Dept. Matt Miller Research Staff Member NEC Labs America

  2. Modern Tools for Oncology • Detailed diagnostics • Targeted therapies • Methods of mapping diagnostic results to therapies (“personalized medicine”) • Methods of testing these tools

  3. How Machine Learning Fits In • Detailed diagnostics • Pattern recognition • Targeted therapies • Machine-assisted drug design • Methods of mapping diagnostic results to therapies (“personalized medicine”) • Cocktail design • Methods of testing these tools • Statistical learning theory

  4. How Machine Learning Fits In • Detailed diagnostics • Pattern recognition • Targeted therapies • Machine-assisted drug design • Methods of mapping diagnostic results to therapies (“personalized medicine”) • Cocktail design • Methods of testing these tools • Statistical learning theory

  5. NEC’s Digital Pathology System • Given a digitized pathology image, determine whether benign or malignant. • Initial application: double-check diagnoses of human pathologists. • First implemented for gastric cancer. • Current development work: • Colon cancer • Breast cancer • Prostrate cancer

  6. NEC’s Digital Pathology System ROI’s Image of whole tissue Lo-res analysis (decision-tree like method) Malignant/ Benign Hi-res analysis (SVM’s, CNN’s)

  7. NEC’s Digital Pathology System • Gastric system tested on 1905 biopsies. • Compared against diagnoses by three human pathologists. • 100 malignant cases • Results: • 227 / 1805 (12.6%) false positives • 1 / 100 false negative

  8. Cocktail Design • Assume there is an unknown function • Find a function Such that is good

  9. Cocktail Design: We Have … • Some theoretical results due to Vapnik. • Proposal for an algorithm. • Methods for handling real-world problems • Limits on what machine may try while learning. • Paucity of real cases. • No data to work on.

  10. Vladimir Vapnik Léon Bottou Eric Cosatto Christopher Malon Matt Miller NEC ML People Here Today

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