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Deep learning accelerates whole slide imaging for next-generation digital pathology applications

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Light(Science & Applications) 2022年第11期11卷 2558-2560页

作者： yair rivenson Aydogan Ozcan Pictor Labs InLos AngelesUSA Electrical and Computer Engineering Department University of CaliforniaLos Angeles 90095 CAUSA Bioengineering Department University of CaliforniaLos Angeles 90095 CAUSA California NanoSystems Institute(CNSI) University of CaliforniaLos Angeles 90095 CAUSA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos Angeles 90095 CAUSA

Deep learning demonstrates the ability to significantly increase the scanning speed of whole slide imaging in *** transformative solution can be used to further accelerate the adoption of digital pathology.

关键词： Deep learning slide

All-optical information-processing capacity of diffractive surfaces

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Light(Science & Applications) 2021年第2期10卷 217-233页

作者： Onur Kulce Deniz Mengu yair rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA

The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and *** 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 ***,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 *** 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 *** 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 *** 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.

关键词： surface. valued optics

All-optical synthesis of an arbitrary linear transformation using diffractive surfaces

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Light(Science & Applications) 2021年第10期10卷 1928-1948页

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 ***,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),***,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 *** 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 *** 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 *** 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 ***,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

关键词： arbitrary valued unitary

Recurrent neural network-based volumetric fluorescence microscopy

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Light(Science & Applications) 2021年第4期10卷 620-635页

作者： Luzhe Huang Hanlong Chen Yilin Luo yair rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA David Geffen School of Medicine University of CaliforniaLos AngelesCA 90095USA

Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical,medical and life *** 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 *** 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 *** experiments on *** 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 *** 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 *** 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 ***-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.

关键词： neural network networks

Ensemble learning of diffractive optical networks

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Light(Science & Applications) 2021年第1期10卷 123-135页

作者： Md Sadman Sakib Rahman Jingxi Li Deniz Mengu yair rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine ***,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation *** deep neural networks(D^(2)NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D^(2)NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and ***,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble *** independently training 1252 D^(2)NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D^(2)NNs that collectively improved the image classification *** this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D^(2)NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D^(2)NNs within each *** results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.

关键词： networks collective successive

Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

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Light(Science & Applications) 2020年第1期9卷 1273-1285页

作者： Yijie Zhang Kevin de Haan yair rivenson Jingxi Li Apostolos Delis Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA Department of Computer Science University of CaliforniaLos AngelesCA 90095USA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos AngelesCA 90095USA

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 ***,this process is time consuming,labour intensive,expensive and destructive to the ***,the ability to virtually stain unlabelled tissue sections,entirely avoiding the histochemical staining step,has been demonstrated using tissue-stain-specific deep neural ***,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 *** 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 *** digital staining matrix is also used to virtually blend existing stains,digitally synthesizing new histological *** 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 *** 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.

关键词： network rendering synthesis

Class-specific differential detection in diffractive optical neural networks improves inference accuracy

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Advanced Photonics 2019年第4期1卷 2-14页

作者： Jingxi Li Deniz Mengu Yi Luo yair rivenson Aydogan Ozcan University of California at Los Angeles Department of Electrical and Computer EngineeringLos AngelesCaliforniaUnited States University of California at Los Angeles Department of BioengineeringLos AngelesCaliforniaUnited States University of California at Los Angeles California NanoSystems InstituteLos AngelesCaliforniaUnited States

Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine *** 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 *** demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light *** 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 *** 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,***,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 *** further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural *** 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 n

关键词： optical computation optical neural networks deep learning optical machine learning diffractive deep neural networks

Deep learning in holography and coherent imaging

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Light(Science & Applications) 2019年第1期8卷 437-444页

作者： yair rivenson Yichen Wu Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California Nano Systems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos AngelesCA 90095USA

Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time *** 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 *** recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications.

关键词： coherent holographic overcome

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

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Light(Science & Applications) 2020年第1期9卷 890-906页

作者： Hongda Wang Hatice Ceylan Koydemir Yunzhe Qiu Bijie Bai Yibo Zhang Yiyin Jin Sabiha Tok Enis Cagatay Yilmaz Esin Gumustekin yair rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA Department of Biophysics Istanbul Medical FacultyIstanbul UniversityIstanbul 22000Turkey Department of Microbiology Immunologyand Molecular GeneticsUniversity of CaliforniaLos AngelesCA 90095USA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos AngelesCA 90095USA

Early identification of pathogenic bacteria in food,water,and bodily fluids is very important and yet challenging,owing to sample complexities and large sample volumes that need to be rapidly *** screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time,accuracy/sensitivity,cost,and sample preparation ***,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 analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding *** performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria(i.e.,Klebsiella aerogenes and Klebsiella pneumoniae ***)in water samples,shortening the detection time by >12 h compared to the Environmental Protection Agency(EPA)-approved *** the preincubation of samples in growth media,our system achieved a limit of detection(LOD)of ~1 colony forming unit(CFU)/L in≤9 h of total test *** platform is highly cost-effective(~$0.6/test)and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface,making it highly suitable for integration with the existing methods currently used for bacteria detection on agar *** by deep learning,this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

关键词： coherent Early lapse