Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation

Kazuya Nishimura, Junya Hayashida, Chenyang Wang, Dai Fei Elmer Ker, Ryoma Bise

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

We propose a weakly-supervised cell tracking method that can train a convolutional neural network (CNN) by using only the annotation of “cell detection” (i.e., the coordinates of cell positions) without association information, in which cell positions can be easily obtained by nuclear staining. First, we train co-detection CNN that detects cells in successive frames by using weak-labels. Our key assumption is that co-detection CNN implicitly learns association in addition to detection. To obtain the association, we propose a backward-and-forward propagation method that analyzes the correspondence of cell positions in the outputs of co-detection CNN. Experiments demonstrated that the proposed method can associate cells by analyzing co-detection CNN. Even though the method uses only weak supervision, the performance of our method was almost the same as the state-of-the-art supervised method. Code is publicly available in https://github.com/naivete5656/WSCTBFP.

本文言語英語
ホスト出版物のタイトルComputer Vision – ECCV 2020 - 16th European Conference, Proceedings
編集者Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
出版社Springer Science and Business Media Deutschland GmbH
ページ104-121
ページ数18
ISBN(印刷版)9783030586096
DOI
出版ステータス出版済み - 2020
イベント16th European Conference on Computer Vision, ECCV 2020 - Glasgow, 英国
継続期間: 8 23 20208 28 2020

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12357 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

会議

会議16th European Conference on Computer Vision, ECCV 2020
国/地域英国
CityGlasgow
Period8/23/208/28/20

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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