Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues.
Published in Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX, 2021
Recommended citation: Polleys, C. M., Lymperopoulos, P., Thieu, H.-T., Genega, E., Liu, L., & Georgakoudi, I. (2021). Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues. Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX, 11647, 116470Z. https://spie.org/Publications/Proceedings/Paper/10.1117/12.2578650
Using denoising autoencoders to accelerate lable-free medical imaging.
Recommended citation: Polleys, C. M., Lymperopoulos, P., Thieu, H.-T., Genega, E., Liu, L., & Georgakoudi, I. (2021). Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues. Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX, 11647, 116470Z.