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月 1
イベント5th Asian Conference on Pattern Recognition,ACPR 2019 - Auckland, New Zealand
継続期間: 2019 11月 262019 11月 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
国/地域New Zealand
CityAuckland
Period19/11/2619/11/29

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 数学 (全般)

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