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 language | English |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Subtitle of host publication | CVPR 2020 |
Editors | Ce Liu, Greg Mori, Kate Saenko, Silvio Savarese |
Place of Publication | USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 4242-4251 |
Number of pages | 10 |
ISBN (Print) | 9781728171685 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2020 - Virtual, China Duration: 14 Jun 2020 → 19 Jun 2020 http://cvpr2020.thecvf.com (Website ) https://openaccess.thecvf.com/CVPR2020 (Proceedings) https://ieeexplore.ieee.org/xpl/conhome/9142308/proceeding (Proceedings) |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2020 |
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Abbreviated title | CVPR 2020 |
Country/Territory | China |
City | Virtual |
Period | 14/06/20 → 19/06/20 |
Internet address |
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