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| 1 | PhaseStain:the digital staining of label-free quantitative phase microscopy images using deep learning显示文摘Using a deep neural network,we demonstrate a digital staining technique,which we term PhaseStain,to transform the quantitative phase images(QPI)of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained.Through pairs of image data(QPI and the corresponding brightfield images,acquired after staining),we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin,kidney,and liver tissue,matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin,Jones’stain,and Masson’s trichrome stain,respectively.This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general,by eliminating the need for histological staining,reducing sample preparation related costs and saving time.Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning. | Yair Rivenson Tairan Liu Zhensong Wei Yibo Zhang Kevin de Haan Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 19 |
| 2 | Deep learning in holography and coherent imaging显示文摘Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance.Through data-driven approaches,these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography.These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications. | Yair Rivenson Yichen Wu Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 16 |
| 3 | Computational imaging without a computer:seeing through random diffusers at the speed of light显示文摘Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers.Here,we present a computer-free,all-optical image reconstruction method to see through random diffusers at the speed of light.Using deep learning,a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown,random phase diffusers.After the training stage,which is a one-time effort,the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown,new phase diffuser.We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown,random diffusers,never used during training.Unlike digital methods,all-optical diffractive reconstructions do not require power except for the illumination light.This diffractive solution to see through diffusers can be extended to other wavelengths,and might fuel various applications in biomedical imaging,astronomy,atmospheric sciences,oceanography,security,robotics,autonomous vehicles,among many others. | Yi Luo Yifan Zhao Jingxi Li Ege Çetintaş Yair Rivenson Mona Jarrahi Aydogan Ozcan | 2022 | eLight2022，2，1： | 16 |
| 4 | A deep learning-enabled portable imaging flow cytometer for cost-effective, highthroughput, and label-free analysis of natural water samples显示文摘We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h.The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel.These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling.Motion blur is eliminated by simultaneously illuminating the sample with red,green,and blue light-emitting diodes that are pulsed.Operated by a laptop computer,this portable device measures 15.5 cm×15 cm×12.5 cm,weighs 1 kg,and compared to standard imaging flow cytometers,it provides extreme reductions of cost,size and weight while also providing a high volumetric throughput over a large object size range.We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro-and nanoplankton composition.Furthermore,we measured the concentration of a potentially toxic alga(Pseudo-nitzschia)in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health.The cost-effectiveness,compactness,and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for largescale and continuous monitoring of the ocean microbiome,including its plankton composition. | Zoltán Gӧrӧcs Miu Tamamitsu Vittorio Bianco Patrick Wolf Shounak Roy Koyoshi Shindo Kyrollos Yanny Yichen Wu Hatice Ceylan Koydemir Yair Rivenson Aydogan Ozcan | 2018 | Light(Science & Applications)2018，7，1： | 14 |
| 5 | Bright-field holography:cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram显示文摘Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram.However,unlike a conventional bright-field microscopy image,the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects.Here,we demonstrate that cross-modality deep learning using a generative adversarial network(GAN)can endow holographic images of a sample volume with bright-field microscopy contrast,combining the volumetric imaging capability of holography with the speckle-and artifact-free image contrast of incoherent bright-field microscopy.We illustrate the performance of this“bright-field holography”method through the snapshot imaging of bioaerosols distributed in 3D,matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope.This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging,and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram,benefiting from the wave-propagation framework of holography. | Yichen Wu Yilin Luo Gunvant Chaudhari Yair Rivenson Ayfer Calis Kevin de Haan Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 12 |
| 6 | Design of task-specific optical systems using broadband diffractive neural networks显示文摘Deep learning has been transformative in many fields,motivating the emergence of various optical computing architectures.Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks.Diffraction-based all-optical object recognition systems,designed through this framework and fabricated by 3D printing,have been reported to recognize handwritten digits and fashion products,demonstrating all-optical inference and generalization to sub-classes of data.These previous diffractive approaches employed monochromatic coherent light as the illumination source.Here,we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning.We experimentally validated the success of this broadband diffractive neural network architecture by designing,fabricating and testing seven different multi-layer,diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize(1)a series of tuneable,single-passband and dual-passband spectral filters and(2)spatially controlled wavelength de-multiplexing.Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy,broadband diffractive neural networks help us engineer the light–matter interaction in 3D,diverging from intuitive and analytical design methods to create taskspecific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning. | Yi Luo Deniz Mengu Nezih T.Yardimci Yair Rivenson Muhammed Veli Mona Jarrahi Aydogan Ozcan | 2019 | Light(Science & Applications)2019，8，1： | 9 |
| 7 | Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue显示文摘Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections,rendering the tissue constituents visible for microscopic analysis by medical experts.However,this process is time consuming,labour intensive,expensive and destructive to the specimen.Recently,the ability to virtually stain unlabelled tissue sections,entirely avoiding the histochemical staining step,has been demonstrated using tissue-stain-specific deep neural networks.Here,we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images,in which different stains are merged following a micro-structure map defined by the user.This approach uses a single deep neural network that receives two different sources of information as its input:(1)autofluorescence images of the label-free tissue sample and(2)a“digital staining matrix”,which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section.This digital staining matrix is also used to virtually blend existing stains,digitally synthesizing new histological stains.We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin(H&E),Jones’silver stain,and Masson’s trichrome stain.Using a single network,this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section,which is currently not feasible with standard histochemical staining methods. | Yijie Zhang Kevin de Haan Yair Rivenson Jingxi Li Apostolos Delis Aydogan Ozcan | 2020 | Light(Science & Applications)2020，9，1： | 7 |
| 8 | Class-specific differential detection in diffractive optical neural networks improves inference accuracy显示文摘Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine learning.Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference,achieving promising performance for object classification and imaging.We demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity.In this differential detection scheme,each class is assigned to a separate pair of detectors,behind a diffractive optical network,and the class inference is made by maximizing the normalized signal difference between the photodetector pairs.Using this differential detection scheme,involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons,we numerically achieved blind testing accuracies of 98.54%,90.54%,and 48.51%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Moreover,by utilizing the inherent parallelization capability of optical systems,we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in parallel.We further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural networks.Using this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel,our simulations achieved blind testing accuracies of 98.52%,91.48%,and 50.82%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively,coming close to the performance of some of the earlier generations of all-electronic deep neural networks,e.g.,LeNet,which achieves classification accuracies of 98.77%,90.27%,and 55.21%corresponding to the same datasets,respectively.In addition to these jointly optimized diffractive neural networks,we also independently optimized multiple diffractive networks and utilized them in a way that is similar to ensemble methods practiced in machine learning;using 3 independently optimized differential diffractive neural networks that optically project their light onto a common output/detector plane,we numerically achieved blind testing accuracies of 98.59%,91.06%,and 51.44%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Through these systematic advances in designing diffractive neural networks,the reported classification accuracies set the state of the art for all-optical neural network design.The presented framework might be useful to bring optical neural network-based low power solutions for various machine learning applications and help us design new computational cameras that are task-specific. | Jingxi Li Deniz Mengu Yi Luo Yair Rivenson Aydogan Ozcan | 2019 | Advanced Photonics2019，1，4： | 6 |
| 9 | Phase recovery and holographic image reconstruction using deep learning in neural networks显示文摘Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography.In this study,we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training.This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference-related spatial artifacts.This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram,requiring fewer measurements in addition to being computationally faster.We validated this method by reconstructing the phase and amplitude images of various samples,including blood and Pap smears and tissue sections.These results highlight that challenging problems in imaging science can be overcome through machine learning,providing new avenues to design powerful computational imaging systems. | Yair Rivenson Yibo Zhang Harun Günaydın Da Teng Aydogan Ozcan | 2017 | Light(Science & Applications)2017，6，1： | 5 |
| 10 | Emerging Advances to Transform Histopathology Using Virtual Staining显示文摘In an age where digitization is widespread in clinical and preclinical workflows,pathology is still predominantly practiced by microscopic evaluation of stained tissue specimens affixed on glass slides.Over the last decade,new high throughput digital scanning microscopes have ushered in the era of digital pathology that,along with recent advances in machine vision,have opened up new possibilities for Computer-Aided-Diagnoses.Despite these advances,the high infrastructural costs related to digital pathology and the perception that the digitization process is an additional and nondirectly reimbursable step have challenged its widespread adoption.Here,we discuss how emerging virtual staining technologies and machine learning can help to disrupt the standard histopathology workflow and create new avenues for the diagnostic paradigm that will benefit patients and healthcare systems alike via digital pathology. | Yair Rivenson Kevin de Haan W.Dean Wallace Aydogan Ozcan | 2020 | Biomedical Engineering Frontiers2020，1，1： | 4 |
| 11 | Recurrent neural network-based volumetric fluorescence microscopy显示文摘Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical,medical and life sciences.Here we report a deep learning-based volumetric image inference framework that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume.Through a recurrent convolutional neural network,which we term as Recurrent-MZ,2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field.Using experiments on C.elegans and nanobead samples,Recurrent-MZ is demonstrated to significantly increase the depth-of-field of a 63×/1.4NA objective lens,also providing a 30-fold reduction in the number of axial scans required to image the same sample volume.We further illustrated the generalization of this recurrent network for 3D imaging by showing its resilience to varying imaging conditions,including e.g.,different sequences of input images,covering various axial permutations and unknown axial positioning errors.We also demonstrated wide-field to confocal cross-modality image transformations using Recurrent-MZ framework and performed 3D image reconstruction of a sample using a few wide-field 2D fluorescence images as input,matching confocal microscopy images of the same sample volume.Recurrent-MZ demonstrates the first application of recurrent neural networks in microscopic image reconstruction and provides a flexible and rapid volumetric imaging framework,overcoming the limitations of current 3D scanning microscopy tools. | Luzhe Huang Hanlong Chen Yilin Luo Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，4： | 4 |
| 12 | All-optical synthesis of an arbitrary linear transformation using diffractive surfaces显示文摘Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical components.Here,we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input(Ni)and output(No),where Ni and No represent the number of pixels at the input and output fields-of-view(FOVs),respectively.First,we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation.In addition to this data-free design approach,we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation.We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven(deep learning-based)diffractive designs to all-optically perform(i)arbitrarily-chosen complex-valued transformations including unitary,nonunitary,and noninvertible transforms,(ii)2D discrete Fourier transformation,(iii)arbitrary 2D permutation operations,and(iv)high-pass filtered coherent imaging.Our analyses reveal that if the total number(N)of spatially-engineered diffractive features/neurons is≥Ni×No,both design methods succeed in all-optical implementation of the target transformation,achieving negligible error.However,compared to data-free designs,deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N | Onur Kulce Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，10： | 3 |
| 13 | All-optical information-processing capacity of diffractive surfaces显示文摘The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics.These advances related to the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine-learning tasks through light-matter interactions and diffraction.Here,we analyze the information-processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output field-of-view.We show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network,up to a limit that is dictated by the extent of the input and output fields-of-view.Deeper diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher-dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view and exhibit depth advantages in terms of their statistical inference,learning,and generalization capabilities for different image classification tasks when compared with a single trainable diffractive surface.These analyses and conclusions are broadly applicable to various forms of diffractive surfaces,including,e.g.,plasmomc and/or dielectric-based metasurfaces and flat optics,which can be used to form all-optical processors. | Onur Kulce Deniz Mengu Yair Rivenson Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，2： | 3 |
| 14 | Bifidobacterium longum,a lactic acid-producing intestinal bacterium inhibits colon cancer and modulates the intermediate biomarkers of colon carcinogenesis显示文摘 | SINGH J RIVENSON A TOMITA M etc al | 1997 | Carcinogenesis1997，18，4： | 1 |
| 15 | Effect of the fat content of the diet on the carcinogenic activity of 4-(methylnitrosamino)-1-(3-pyri-dyl)-l-butanone in F344 rats显示文摘 | HOFFMANN D RIVENSON A ABBI R | | 0，，： | 1 |
| 16 | Chemoprevention of colon carcinogenesis by organosulfur compounds显示文摘 | Reddy B S Rao C V Rivenson A | 1993 | Cancer Res1993，53，： | 1 |
| 17 | Compressed imaging with a separable sensing operator显示文摘 | RIVENSON Y STERN A | 2009 | IEEE Signal Processing Letters2009，16，6： | 1 |
| 18 | Dose-response study of the carcinogenicity of tobacco-specific N-nitrosamines in F344 rats显示文摘 | HOFFMANN D RIVENSON A AMIN S | 1984 | Jounalof Cancer Research and Clinical Oncology1984，108，： | 1 |
| 19 | Bifidobacteriumlongum,a lacticacid-producing intestinal inhibit colon cancer and modulatethe intermediate biomarkers of colon carcinogenesis 显示文摘 | Singh J Rivenson A Tomita M Shimamura S Ishibashi N Reddy B S | 1997 | Carcinogenesis1997，18，： | 1 |
| 20 | Biopsy-free in vivo virtual histology of skin using deep learning显示文摘An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors.The process is cumbersome and time-consuming,often leading to unnecessary biopsies and scars.Emerging noninvasive optical technologies such as reflectance confocal microscopy(RCM)can provide label-free,cellular-level resolution,in vivo images of skin without performing a biopsy.Although RCM is a useful diagnostic tool,it requires specialized training because the acquired images are grayscale,lack nuclear features,and are difficult to correlate with tissue pathology.Here,we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution,enabling visualization of the epidermis,dermal-epidermal junction,and superficial dermis layers.The network was trained under an adversarial learning scheme,which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth.We show that this trained neural network can be used to rapidly perform virtual histology of in vivo,label-free RCM images of normal skin structure,basal cell carcinoma,and melanocytic nevi with pigmented melanocytes,demonstrating similar histological features to traditional histology from the same excised tissue.This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies. | Jingxi Li Jason Garfinkel Xiaoran Zhang Di Wu Yijie Zhang Kevin de Haan Hongda Wang Tairan Liu Bijie Bai Yair Rivenson Gennady Rubinstein Philip O.Scumpia Aydogan Ozcan | 2021 | Light(Science & Applications)2021，10，12： | 1 |
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