 Connection
Fuyong Xing to Microscopy
This is a "connection" page, showing publications Fuyong Xing has written about Microscopy.
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Connection Strength |
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3.139 |
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Xing F, Cornish TC, Bennett TD, Ghosh D. Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images. IEEE Trans Med Imaging. 2021 10; 40(10):2880-2896.
Score: 0.673
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Xing F, Xie Y, Shi X, Chen P, Zhang Z, Yang L. Towards pixel-to-pixel deep nucleus detection in microscopy images. BMC Bioinformatics. 2019 Sep 14; 20(1):472.
Score: 0.584
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Xing F, Shi X, Zhang Z, Cai J, Xie Y, Yang L. Transfer Shape Modeling Towards High-throughput Microscopy Image Segmentation. Med Image Comput Comput Assist Interv. 2016 Oct; 9902:183-190.
Score: 0.476
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Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng. 2016; 9:234-63.
Score: 0.452
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Xing F, Yang L. Robust selection-based sparse shape model for lung cancer image segmentation. Med Image Comput Comput Assist Interv. 2013; 16(Pt 3):404-12.
Score: 0.367
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Xing F, Yang X, Cornish TC, Ghosh D. Learning with limited target data to detect cells in cross-modality images. Med Image Anal. 2023 12; 90:102969.
Score: 0.193
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Xing F, Cornish TC, Bennett T, Ghosh D, Yang L. Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images. IEEE Trans Biomed Eng. 2019 11; 66(11):3088-3097.
Score: 0.140
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Xie Y, Kong X, Xing F, Liu F, Su H, Yang L. Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images. Med Image Comput Comput Assist Interv. 2015 Oct; 9351:374-382.
Score: 0.112
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Zhang X, Xing F, Su H, Yang L, Zhang S. High-throughput histopathological image analysis via robust cell segmentation and hashing. Med Image Anal. 2015 Dec; 26(1):306-15.
Score: 0.112
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Sapkota M, Liu F, Xie Y, Su H, Xing F, Yang L. AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle. IEEE J Biomed Health Inform. 2018 05; 22(3):942-954.
Score: 0.031
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Connection Strength
The connection strength for concepts is the sum of the scores for each matching publication.
Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
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