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Automated multi-class ground-truth labeling of H&E images for deep learning using multiplexed fluorescence microscopy.

, , , , , , и . Medical Imaging: Digital Pathology, том 10956 из SPIE Proceedings, стр. 109560J. SPIE, (2019)

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Gayathri Nadarajan

 

Другие публикации лиц с тем же именем

Realistic cross-domain microscopy via conditional generative adversarial networks: converting immunofluorescence to hematoxylin and eosin., и . Medical Imaging: Digital Pathology, том 11320 из SPIE Proceedings, стр. 113200S. SPIE, (2020)Automated multi-class ground-truth labeling of H&E images for deep learning using multiplexed fluorescence microscopy., , , , , , и . Medical Imaging: Digital Pathology, том 10956 из SPIE Proceedings, стр. 109560J. SPIE, (2019)Conditional generative adversarial networks for H&E to IF domain transfer: experiments with breast and prostate cancer., и . Medical Imaging: Digital Pathology, том 11603 из SPIE Proceedings, SPIE, (2021)