2.6k citations · 9.9k across the 41 of their papers we have counts for
10 papers · 1 filter
Cross-modality deep learning brings bright-field microscopy contrast to holography
Yichen Wu, Yilin Luo, Gunvant Chaudhari +4
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artif…
Deep learning-based super-resolution in coherent imaging systems
Tairan Liu, Kevin de Haan, Yair Rivenson +4
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can…
Response to Comment on "All-optical machine learning using diffractive deep neural networks"
Deniz Mengu, Yi Luo, Yair Rivenson +3
In their Comment, Wei et al. (arXiv:1809.08360v1 [cs.LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the s…
Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
Deniz Mengu, Yi Luo, Yair Rivenson +1
Optical machine learning offers advantages in terms of power efficiency, scalability and computation speed. Recently, an optical machine learning method based on Diffractive Deep N…
Accurate color imaging of pathology slides using holography and absorbance spectrum estimation of histochemical stains
Yibo Zhang, Tairan Liu, Yujia Huang +6
Holographic microscopy presents challenges for color reproduction due to the usage of narrow-band illumination sources, which especially impacts the imaging of stained pathology sl…
PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning
Yair Rivenson, Tairan Liu, Zhensong Wei +2
Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform quantitative phase images (QPI) of labelfree tissue sections into i…
Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction
Yair Rivenson, Aydogan Ozcan
We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations amo…
All-Optical Machine Learning Using Diffractive Deep Neural Networks
Xing Lin, Yair Rivenson, Nezih T. Yardimci +3
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractiv…
Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
Yair Rivenson, Hongda Wang, Zhensong Wei +3
Histological analysis of tissue samples is one of the most widely used methods for disease diagnosis. After taking a sample from a patient, it goes through a lengthy and laborious…
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Yichen Wu, Yair Rivenson, Yibo Zhang +4
Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s),…