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20192023
most citedDeep Learning-enabled Virtual Histological Staining of Biological Samples

354 citations · 2.8k across the 40 of their papers we have counts for

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Showing 2022Show all

18 papers · 1 filter

physics.optics2022★ 59 cited

Data class-specific all-optical transformations and encryption

Bijie Bai, Heming Wei, Xilin Yang +2

Diffractive optical networks provide rich opportunities for visual computing tasks since the spatial information of a scene can be directly accessed by a diffractive processor with…

physics.optics2022★ 110 cited

Snapshot Multispectral Imaging Using a Diffractive Optical Network

Deniz Mengu, Anika Tabassum, Mona Jarrahi +1

Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network…

physics.optics2022★ 57 cited

Unidirectional Imaging using Deep Learning-Designed Materials

Jingxi Li, Tianyi Gan, Yifan Zhao +5

A unidirectional imager would only permit image formation along one direction, from an input field-of-view (FOV) A to an output FOV B, and in the reverse path, the image formation…

physics.med-ph2022★ 354 cited

Deep Learning-enabled Virtual Histological Staining of Biological Samples

Bijie Bai, Xilin Yang, Yuzhu Li +3

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromat…

cs.CV2022★ 106 cited

Self-supervised learning of hologram reconstruction using physics consistency

Luzhe Huang, Hanlong Chen, Tairan Liu +1

The past decade has witnessed transformative applications of deep learning in various computational imaging, sensing and microscopy tasks. Due to the supervised learning schemes em…

physics.app-ph2022★ 21 cited

Virtual impactor-based label-free bio-aerosol detection using holography and deep learning

Yi Luo, Yijie Zhang, Tairan Liu +3

Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and qu…

physics.optics2022★ 21 cited

Time-lapse image classification using a diffractive neural network

Md Sadman Sakib Rahman, Aydogan Ozcan

Diffractive deep neural networks (D2NNs) define an all-optical computing framework comprised of spatially engineered passive surfaces that collectively process optical input inform…

cs.NE2022★ 90 cited

Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network

Jingxi Li, Bijie Bai, Yi Luo +1

We report deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily-selected, complex-valued…

physics.med-ph2022★ 44 cited

Virtual stain transfer in histology via cascaded deep neural networks

Xilin Yang, Bijie Bai, Yijie Zhang +4

Pathological diagnosis relies on the visual inspection of histologically stained thin tissue specimens, where different types of stains are applied to bring contrast to and highlig…

eess.IV2022★ 28 cited

Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks

Yijie Zhang, Luzhe Huang, Tairan Liu +5

Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming,…

physics.ins-det2022★ 56 cited

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning

Tairan Liu, Yuzhu Li, Hatice Ceylan Koydemir +8

We present a rapid and stain-free quantitative viral plaque assay using lensfree holographic imaging and deep learning. This cost-effective, compact, and automated device significa…

physics.optics2022★ 1 cited

Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks

Deniz Mengu, Yifan Zhao, Anika Tabassum +2

Permutation matrices form an important computational building block frequently used in various fields including e.g., communications, information security and data processing. Opti…

physics.optics2022★ 88 cited

To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects

Bijie Bai, Yi Luo, Tianyi Gan +6

Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing…

physics.ins-det2022★ 29 cited

Deep Learning-enabled Detection and Classification of Bacterial Colonies using a Thin Film Transistor (TFT) Image Sensor

Yuzhu Li, Tairan Liu, Hatice Ceylan Koydemir +9

Early detection and identification of pathogenic bacteria such as Escherichia coli (E. coli) is an essential task for public health. The conventional culture-based methods for bact…

physics.optics2022★ 17 cited

Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers

Yuhang Li, Yi Luo, Bijie Bai +1

Imaging through diffusive media is a challenging problem, where the existing solutions heavily rely on digital computers to reconstruct distorted images. We provide a detailed anal…

cs.CV2022★ 111 cited

Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization

Hanlong Chen, Luzhe Huang, Tairan Liu +1

Deep learning-based image reconstruction methods have achieved remarkable success in phase recovery and holographic imaging. However, the generalization of their image reconstructi…

physics.optics2022★ 117 cited

Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network

Jingxi Li, Yi-Chun Hung, Onur Kulce +2

Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning. Among different approaches, diffractive optical n…

eess.IV2022★ 23 cited

Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network

Luzhe Huang, Xilin Yang, Tairan Liu +1

Deep learning-based methods in computational microscopy have been shown to be powerful but in general face some challenges due to limited generalization to new types of samples and…