Date of Award
Spring 5-2-2025
Document Type
Thesis
Degree Name
Master of Arts (MA)
Department
Physics and Astronomy
First Advisor
Min Xu
Second Advisor
Ying-Chih Chen
Academic Program Adviser
Mark Hillery
Abstract
We present a regularized CycleGAN with a Dense Residual U-Net to virtually stain autofluorescence images of tissue into H&E-like images. Our method outperforms standard architectures, reduces artifacts, and achieves superior FID scores, enabling efficient, label-free, and accurate digital pathology for unpaired datasets using multi-channel fluorescence inputs.
Recommended Citation
Wen, Zhesi, "Unpaired Virtual Histological Staining of Tissue from Autofluorescence Using Regularized Cycle-Consistent Adversarial Networks" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/1302
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