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Automatic detection of colorectal cancer from images using active learning

Research Project

Project/Area Number 25330337
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Research Field Life / Health / Medical informatics
Research InstitutionHiroshima University

Principal Investigator

Raytchev Bisser  広島大学, 工学(系)研究科(研究院), 助教 (00531922)

Project Period (FY) 2013-04-01 – 2016-03-31
Project Status Completed (Fiscal Year 2015)
Budget Amount *help
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2013: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Keywords大腸癌 / 癌の自動診断システム / NBI画像 / Active Learning / Ensemble methods / NBI画像 / Active learning
Outline of Final Research Achievements

An automatic diagnosis method for colorectal cancer from Narrow Band Imaging (NBI) has been developed, based on local context features and randomized decision forests. The local context features are based on a texton map from which texture and local context-based information is extracted from the surrounding area centered on each pixel. The features are very high-dimensional (infinite in principle) and therefore very discriminative, which combined with their huge number and the ability of random forests to handle efficiently such data without over-fitting enables us to achieve very good accuracy from a very small number of training images. Additionally, by providing local pixel-level classification the resulting method is much more general and does not depend on the concrete configuration of patterns available in the training images. The method operates locally and therefore is much better suited for video data also, which makes it applicable to more realistic diagnostic scenarios.

Report

(4 results)
  • 2015 Annual Research Report   Final Research Report ( PDF )
  • 2014 Research-status Report
  • 2013 Research-status Report
  • Research Products

    (4 results)

All 2015 2013

All Journal Article (1 results) (of which Peer Reviewed: 1 results,  Acknowledgement Compliant: 1 results) Presentation (3 results)

  • [Journal Article] Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features2015

    • Author(s)
      A. Masuda, B. Raytchev, T. Kurita, T. Imamura, M. Suzuki, T. Tamaki and K. Kaneda
    • Journal Title

      Lecture Notes in Computer Science (LNCS)

      Volume: 9352 Pages: 153-160

    • DOI

      10.1007/978-3-319-24888-2_19

    • ISBN
      9783319248875, 9783319248882
    • Related Report
      2015 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Presentation] 局所特徴量を用いたiPS細胞の分化・未分化検出2015

    • Author(s)
      増田 淳基, Bisser Raytchev, 栗田 多喜夫, 今村 享, 鈴木 理, 玉木 徹, 金田 和文
    • Organizer
      第18回画像の認識・理解シンポジウム(MIRU2015)
    • Place of Presentation
      ホテル阪急エキスポパーク, 大阪
    • Year and Date
      2015-07-28
    • Related Report
      2015 Annual Research Report
  • [Presentation] Image Sequence Recognition with Active Learning Using Uncertainty Sampling2013

    • Author(s)
      M. Minakawa, B. Raytchev, T. Tamaki, K. Kaneda
    • Organizer
      IEEE International Joint Conference on Neural Networks (IJCNN2013)
    • Place of Presentation
      Dallas, USA
    • Related Report
      2013 Research-status Report
  • [Presentation] 画像列を用いた物体認識へのActive Learningの適用2013

    • Author(s)
      皆川雅俊, Bisser Raytchev, 玉木徹, 金田和文
    • Organizer
      第19回画像センシングシンポジウム SSII2013
    • Place of Presentation
      横浜
    • Related Report
      2013 Research-status Report

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Published: 2014-07-25   Modified: 2019-07-29  

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