1.1k citations · 3.8k across the 14 of their papers we have counts for
14 papers
Deep learning-based holographic polarization microscopy
Tairan Liu, Kevin de Haan, Bijie Bai +8
Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically…
Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning
Hongda Wang, Hatice Ceylan Koydemir, Yunzhe Qiu +8
We present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60 mm diameter agar-plate and analyzes…
Deep learning-based color holographic microscopy
Tairan Liu, Zhensong Wei, Yair Rivenson +4
We report a framework based on a generative adversarial network (GAN) that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated…
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…
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…
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),…
High-throughput screening of encapsulated islets using wide-field lens-free on-chip imaging
Yibo Zhang, Michael Alexander, Sam Yang +4
Islet microencapsulation is a promising solution to diabetes treatment, but its quality control based on manual microscopic inspection is extremely low-throughput, highly variable…
Spatial mapping and analysis of aerosols during a forest fire using computational mobile microscopy
Yichen Wu, Ashutosh Shiledar, Yi Luo +6
Forest fires are a major source of particulate matter (PM) air pollution on a global scale. The composition and impact of PM are typically studied using only laboratory instruments…
Deep learning enhanced mobile-phone microscopy
Yair Rivenson, Hatice Ceylan Koydemir, Hongda Wang +8
Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the o…
Comparison of Gini index and Tamura coefficient for holographic autofocusing based on the edge sparsity of the complex optical wavefront
Miu Tamamitsu, Yibo Zhang, Hongda Wang +2
The Sparsity of the Gradient (SoG) is a robust autofocusing criterion for holography, where the gradient modulus of the complex refocused hologram is calculated, on which a sparsit…
Deep Learning Microscopy
Yair Rivenson, Zoltan Gorocs, Harun Gunaydin +3
We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its tr…
Phase recovery and holographic image reconstruction using deep learning in neural networks
Yair Rivenson, Yibo Zhang, Harun Gunaydin +2
Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. Here we demonstrate that a neural network can learn to perform phase…