Weakly Supervised Domain Adaptation with Point Supervision in Histopathological Image Segmentation

Shun Obikane, Yoshimitsu Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationPattern Recognition - ACPR 2019 Workshops, Proceedings
EditorsMichael Cree, Fay Huang, Junsong Yuan, Wei Qi Yan
Number of pages14
ISBN (Print)9789811536502
Publication statusPublished - 2020 Jan 1
Event5th Asian Conference on Pattern Recognition,ACPR 2019 - Auckland, New Zealand
Duration: 2019 Nov 262019 Nov 29

Publication series

NameCommunications in Computer and Information Science
Volume1180 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference5th Asian Conference on Pattern Recognition,ACPR 2019
Country/TerritoryNew Zealand


  • Histopathology image segmentation
  • Medical image analysis
  • Semantic segmentation
  • Weakly supervised domain adaptation

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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