2.6k citations · 9.9k across the 41 of their papers we have counts for
10 papers · 1 filter
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
Yijie Zhang, Tairan Liu, Manmohan Singh +4
Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning…
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
Luzhe Huang, Tairan Liu, Xilin Yang +3
Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important st…
Deep learning-based virtual refocusing of images using an engineered point-spread function
Xilin Yang, Luzhe Huang, Yilin Luo +4
We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF). This netwo…
Deep learning-based transformation of the H&E stain into special stains
Kevin de Haan, Yijie Zhang, Jonathan E. Zuckerman +11
Pathology is practiced by visual inspection of histochemically stained slides. Most commonly, the hematoxylin and eosin (H&E) stain is used in the diagnostic workflow and it is the…
All-Optical Information Processing Capacity of Diffractive Surfaces
Onur Kulce, Deniz Mengu, Yair Rivenson +1
Precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics. These advances around the engineering of materials with…
Misalignment Resilient Diffractive Optical Networks
Deniz Mengu, Yifan Zhao, Nezih T. Yardimci +3
As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light-matter intera…
Single-shot autofocusing of microscopy images using deep learning
Yilin Luo, Luzhe Huang, Yair Rivenson +1
We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that…
Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue
Yijie Zhang, Kevin de Haan, Yair Rivenson +3
Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue const…
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…
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…