Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 29, 2018

Abstract

PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this paper, we developed a method for classifying subtypes of lung adenocarcinoma from pathological images using neural network whose that can evaluate phenotypic features from wider area to consider cellular distributions.

Authors

  • Victor Andrew A Antonio
    Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
  • Naoaki Ono
    Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan. nono@is.naist.jp.
  • Akira Saito
    Division of Life Science and Engineering, School of Science and Engineering, Tokyo Denki University (TDU), Ishizaka, Hatoyama-Machi, Hiki-Gun, Saitama, 350-0394, Japan.
  • Tetsuo Sato
    Graduate School of information Science, Nara Institute of Science and Technology, Ikoma, Japan.
  • Md Altaf-Ul-Amin
    Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
  • Shigehiko Kanaya
    Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.

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