Weakly Supervised Domain Adaptation with Point Supervision in Histopathological Image Segmentation

Shun Obikane, Yoshimitsu Aoki

    研究成果: Conference contribution

    抄録

    When a model learned in a domain is applied to a different domain, even if in the same task, there is no guarantee of accuracy. This is a very important issue when deep learning and machine learning are applied in the field. In medical applications, there is a wide variety of domain bias, making it very difficult to create a model appropriate for all domains. Furthermore, semantic segmentation needs fine annotation and its high labor cost makes its application difficult. Histopathological image segmentation enables drug discovery and medical image analysis, but it is expensive due to its annotation cost and the need for the skills of histopathological experts. In this paper, we focus on a weakly supervised method using point annotation unique to histopathological image segmentation, and tackled on weakly supervised domain adaptation to suppress domain gaps. Providing point level annotation instead of fine annotation decreases the high cost of labor normally required.

    本文言語English
    ホスト出版物のタイトルPattern Recognition - ACPR 2019 Workshops, Proceedings
    編集者Michael Cree, Fay Huang, Junsong Yuan, Wei Qi Yan
    出版社Springer
    ページ127-140
    ページ数14
    ISBN(印刷版)9789811536502
    DOI
    出版ステータスPublished - 2020 1 1
    イベント5th Asian Conference on Pattern Recognition,ACPR 2019 - Auckland, New Zealand
    継続期間: 2019 11 262019 11 29

    出版物シリーズ

    名前Communications in Computer and Information Science
    1180 CCIS
    ISSN(印刷版)1865-0929
    ISSN(電子版)1865-0937

    Conference

    Conference5th Asian Conference on Pattern Recognition,ACPR 2019
    CountryNew Zealand
    CityAuckland
    Period19/11/2619/11/29

    ASJC Scopus subject areas

    • Computer Science(all)
    • Mathematics(all)

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