Unsupervised instance segmentation in microscopy images via panoptic domain adaptation and task re-weighting

Dongnan Liu, Donghao Zhang, Yang Song, Fan Zhang, Lauren O’Donnell, Heng Huang, Mei Chen, Weidong Cai

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

34 Citations (Scopus)

Abstract

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift across datasets. In this work, we propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images, by learning from fluorescence microscopy images. More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images. Secondly, a semantic branch with a domain discriminator is designed to achieve panoptic-level domain adaptation. Thirdly, in order to avoid the influence of the source-biased features, we propose a task re-weighting mechanism to dynamically add trade-off weights for the task-specific loss functions. Experimental results on three datasets indicate that our proposed method outperforms state-of-the-art UDA methods significantly, and demonstrates a similar performance as fully supervised methods.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Subtitle of host publicationCVPR 2020
EditorsCe Liu, Greg Mori, Kate Saenko, Silvio Savarese
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4242-4251
Number of pages10
ISBN (Print)9781728171685
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual, China
Duration: 14 Jun 202019 Jun 2020
http://cvpr2020.thecvf.com (Website )
https://openaccess.thecvf.com/CVPR2020 (Proceedings)
https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding (Proceedings)

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2020
Abbreviated titleCVPR 2020
Country/TerritoryChina
CityVirtual
Period14/06/2019/06/20
Internet address

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