Abstract |
Recent digital pathology workflows mainly focus on mono-modality histopathology image analysis. However, they ignore the complementarity between Haematoxylin & Eosin (H&E) and Immunohistochemically (IHC) stained images, which can provide comprehensive gold standard for cancer diagnosis. To resolve this issue, we propose a cross-boosted multi-target domain adaptation pipeline for multi-modality histopathology images, which contains Cross-frequency Style-auxiliary Translation Network (CSTN) and Dual Cross-boosted Segmentation Network (DCSN). Firstly, CSTN achieves the one-to-many translation from fluorescence microscopy images to H&E and IHC images for providing source domain training data. To generate images with realistic color and texture, Cross-frequency Feature Transfer Module (CFTM) is developed to pertinently restructure and normalize high-frequency content and low-frequency style features from different domains. Then, DCSN fulfills multi-target domain adaptive segmentation, where a dual-branch encoder is introduced, and Bidirectional Cross-domain Boosting Module (BCBM) is designed to implement cross-modality information complementation through bidirectional inter-domain collaboration. Finally, we establish Multi-modality Thymus Histopathology (MThH) dataset, which is the largest publicly available H&E and IHC image benchmark. Experiments on MThH dataset and several public datasets show that the proposed pipeline outperforms state-of-the-art methods on both histopathology image translation and segmentation.
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Authors | Huaqi Zhang, Jie Liu, Pengyu Wang, Zekuan Yu, Weifan Liu, Huang Chen |
Journal | IEEE journal of biomedical and health informatics
(IEEE J Biomed Health Inform)
Vol. 26
Issue 7
Pg. 3197-3208
(07 2022)
ISSN: 2168-2208 [Electronic] United States |
PMID | 35196252
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Topics |
- Humans
- Benchmarking
- Image Processing, Computer-Assisted
- Microscopy, Fluorescence
- Workflow
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